High energy physics (HEP) experiments are among the prominent scientific fields facing huge challenges related to processing vast amounts of data. In the quest to explore rare interactions between fundamental particles, these experiments increasingly rely on advanced and distributed computing techniques, especially in light of the anticipated increase in performance demands over the coming years. This article focuses on the urgent need to develop portable programming solutions, enabling these experiments to make the most of the available computing resources, including graphics processing units (GPUs) from various manufacturers. In this context, we will review the experiments and results derived from testing algorithms related to particle tracking, and compare the performance and challenges associated with applying portable programming techniques. Through this work, we aim to highlight the importance of innovation in computing environments and its role in enhancing the capabilities of high energy physics experiments.
Challenges Facing High Energy Physics
High energy physics (HEP) experiments face significant challenges manifested in the need to process gigantic amounts of data resulting from interactions of fundamental particles. The well-known “CMS” experiment at the Large Hadron Collider (LHC) at CERN, for example, processed hundreds of petabytes of detector and Monte Carlo (MC) simulation data during the period from 2015 to 2018. Looking to the future, experiments like “HL-LHC” and “DUNE” pose new challenges in computing capacity where the event rate at “LHC” is expected to increase by a factor of 7.5, meaning that the amounts of data will grow to the exabyte range. Consequently, the need to transition from traditional computing methods in HEP becomes a necessity rather than an option.
Dealing with these massive amounts of data requires intensive research and development, alongside significant changes in the software infrastructure and techniques used for data analysis. One of the key areas that can assist in this transformation is leveraging diverse and parallel computing resources. For instance, previously, LHC experiments relied on traditional x86 CPUs for most of their computing needs. However, in light of the challenges associated with increasing data volumes, experts have begun to make adjustments to their software frameworks to take advantage of high-performance computing (HPC) resources which increasingly rely on graphics processing units (GPUs).
In light of these challenges, it has become evident that developing HEP algorithms to align with the diverse architecture of computing distributions will be required to meet future needs. This requires rewriting a significant portion of the original programming code, which necessitates considerable efforts from multidisciplinary teams. The critical factor here is the choice of available programming tools, which must allow the same source code to run across multiple computing platforms. This is a fundamental requirement in terms of efficiency and the energy used.
The Need for Portable Programming Tools
The need for portable programming tools highlights the importance of developing HEP software in a way that allows the source code to operate efficiently across a variety of different architectures. One of the solutions utilized is compilation-based programming methods, meaning that the code can be linked to libraries or frameworks capable of managing execution details across different architectures. It is important to emphasize that this approach not only optimizes performance but also facilitates code maintenance and alleviates the workload on programming teams dealing with long and complex sequences of code.
Some available solutions include libraries like Kokkos and Alpaka, which provide high-level data structures and parallel execution options. These libraries effectively support HEP data processing, allowing for the reduction of difficulties associated with rewriting and optimization. This enables scientists to focus on physical analysis rather than the intricate details involved in navigating through multiple environments.
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The transformation represents just a stop on the journey of HEP towards innovation. When discussing performance, results can vary significantly based on implementation details. Thus, experiments conducted using reference algorithms are essential for new discoveries, providing a deep understanding of how to effectively leverage tools to enhance performance.
Software Transportation Experiments and Tools Used
The software transportation experiment involves various tools and technologies aimed at improving performance and simplifying the process for developers. One important consideration is that performance may vary based on how memory is organized and the linking strategy. Studies have shown that using advanced strategies in memory management can lead to significant performance improvements even when working on simple applications.
For example, a study conducted on a testing algorithm used to investigate the potential of software transportation shows that by comparing performance with reference algorithms, valuable insights were gained on how to implement various software transportation solutions. This includes modern programming frameworks, such as std::execution::par, which has been included in the C++ standard since C++ 17. It provides a high-level API and design around concurrent loops but does not allow for the low-level optimizations that can be used to enhance performance in native execution.
Many tools and programming environments have been developed, including SYCL, which offers a C++ standard-based programming model, making it easier to include both host code and kernel code in the same source file. This reflects the importance of ongoing research in high-performance programming and how these developments can meet the growing demand for computing in HEP experiments.
Lessons Learned
The rubble presented in research and the experience gained during transportation operations includes a set of important lessons. The first point is the importance of choosing the right tools. Good tools not only facilitate the transportation process but also enable access to new efficiencies. Portable software solutions provide strategic benefits from the outset, allowing freedom to work across multiple platforms without the need for drastic code modifications.
One of the main issues that emerge from the experiments is the need to remain aware of rapid developments in programming fields. Scientists and developers must stay connected to new communities and emerging technologies to ensure optimal performance is achieved. Additionally, it is important to emphasize that each toolkit has advantages and disadvantages that must be considered when making decisions.
Ultimately, it is clear that success in addressing computing challenges in HEP depends on code that is flexible and capable of adapting to future changes. Good organization of development strategies and following an analytical approach to programming will enable scientists to push the boundaries of knowledge in high energy physics, contributing to the advancement of the scientific community as a whole.
Traditional Word Tracking Algorithm and Its Impact on Large Experiments
The traditional word tracking algorithm is considered one of the core techniques used in high-energy physics (HEP) experiments to track particle movement. This algorithm was developed for various purposes related to reconstructing particle trajectories, providing high accuracy in the measurements required to understand particle behavior in experiments like the CMS experiment. The algorithm includes a mathematical model that predicts the particle’s path based on a set of measurements and surrounding noise. Despite its apparent simplicity, this algorithm forms the basis for the particle tracking process as it relies on complex calculations to associate readings with a specific particle and infer its properties.
The performance of this algorithm varies based on several factors, including the use of small databases for more than one path under the experiment’s dome. For instance, standard programs like “propagate to z” or “propagate to r” are used to assess the algorithm’s performance under certain conditions. Both tests represent a convenient environment for drawing conclusions on how to improve operational efficiency and rewrite algorithms to enhance performance. Moreover, the development of these algorithms plays a significant role in the near future for high-acceleration experiments such as LHC.
Project
MKFIT: Improving Performance with Developed Algorithms
The MKFIT project is a collective effort to modernize traditional tracking algorithms used in high-energy physics experiments. This project aims to rewrite the KF algorithm to be more efficient using a multithreaded and vectorized implementation. The new setup is intended to process large amounts of data more quickly, achieving up to double the speed compared to previous applications.
Research indicates that by using parallel processing techniques, performance can be improved in handling thousands of tracks present in a single experiment. Additionally, storing data close together in memory, where corresponding elements are stored in adjacent locations, enhances the algorithm’s ability to leverage SIMD operations, thus accelerating the individual computations for each track.
The success achieved by MKFIT includes significant improvements on multi-core CPUs, representing a real advancement in the pursuit of research related to particle physics. It is important to note that transitioning to portable computing infrastructure like GPUs requires additional efforts, particularly in dealing with irregular memory access patterns.
Challenges in Transferring the MKFIT Algorithm to GPU Processing Units
Despite the clear benefits of using GPUs to accelerate computational processes, transferring MKFIT to graphic computing environments has not been straightforward. Preliminary experiments to move MKFIT to CUDA revealed significant challenges in adjusting data inputs and achieving acceptable performance in terms of time and efficiency. The irregular access patterns to memory during attempts to organize data from different tracks represented the biggest obstacle to successful implementation in the graphical environment.
Through previous experiments, it became clear that in many cases, a comprehensive rewrite of the core code is necessary to achieve the desired performance. Consequently, the focus has been on developing portable tools to maximize the benefit from the available infrastructure, as the p2z project created for this purpose represents a promising experiment in exploring transport techniques in the context of tracking charged particles.
Algorithmic Process Description for Particle Tracking
Particle tracking consists of several critical computational steps, including finding the particle’s stable path, known as the “pathfinding” process. During this process, multiple sets of measurements are tested to find a parallel set that matches the expected helical path of the particle in a magnetic field. Two key points are emphasized in this process: promotion and word-based updating, both of which require intensive computations and involve complex chemical operations related to matrices.
The promotion operations rely on reevaluating the measurement set based on predictive equations, where mathematical steps are used to estimate the particle’s position based on its current measurements. On the other hand, the updating process involves incorporating new measurements to improve the accuracy of estimates. Both are critical steps in the time taken to reconstruct the tracks, making it essential to optimize these processes to enhance overall experiment efficiency.
Conclusions and Future Prospects
The ongoing efforts to improve tracking algorithms in particle physics research represent a bridge toward achieving a greater understanding of the universe’s fundamental components. Technologies like MKFIT and p2z are powerful tools that will help in processing the vast amounts of data generated in future experiments, highlighting the urgent need for investment in new equipment and techniques. Communication between different research teams and experimenting with multiple strategies are fundamental elements in achieving collective goals, underscoring the importance of teamwork in developing effective data analysis tools.
In light of the notable progress made through new technologies and software, it is hoped that these projects will enhance the effective use of graphic computing, enabling scientists to uncover the mysteries of the universe and provide new answers to profound questions in modern physics.
Organization
Paths in Data Teams: The Concept of MPTRK
The process of organizing paths in data construction is one of the fundamental steps in high-performance programming, as it aims to enhance efficiency and speed when executing certain algorithms. The data pattern MPTRK, which is used within the MKFIT algorithm, has been introduced as an effective tool for improving path processing operations. MPTRK refers to a structured framework known as “Structure-of-Arrays,” or Structure-Of-Arrays, where paths are collected into specific batches known as batch size (bsize). This composition enables SIMD concurrent operations across the elements within each batch, promising to enhance system performance on large data transactions.
Determining the batch size (bsize) is a vital aspect that can be optimized depending on the platform used. For example, the optimal size on Graphics Processing Units (GPUs) might be the NVIDIA warp size of 32, while the batch size on Central Processing Units (CPUs) could align with the AVX-512 vector width of 16. To ensure consistency, size 32 is used across all cases. This flexibility in defining sizes enables the development of algorithms to fit the characteristics of different hardware, leading to improved performance.
When examining data storage in the AOSOA pattern following MKFIT methods, we find that the data is stored in a specific arrangement. The first elements of bsize from the arrays are stored contiguously, followed by the second elements, and so on. This arrangement ensures that memory access operations are facilitated and the time taken to retrieve data is minimized. These practices are more evident in the data illustrations for the p2r and p2z systems, which contain 8192 and 9600 paths respectively. The same model applies to the data concerning hits, underscoring the importance of structural organization in accelerating processing operations.
The significance of data organization within the MPTRK framework is reflected in the capabilities that allow systems to maximize parallel processing operations, thereby saving processing time and enhancing efficiency. Thus, the effective transforming of paths into batches within this structure remains a key reason for improving the performance of the MKFIT program and other related algorithms.
Result Tools and Compatibility Representation: Various Techniques
The compatibility tools for parallel programming applications are continuously evolving, with new features and support for compilers and other tools being added regularly. In this context, nine different tools for parallel programming have been tested across four diverse architectures. However, it was challenging to test all possible combinations within the scope of this study.
The final assessment of the p2z and p2r implementations has been presented, where tables illustrate which tools or compilers have been used, along with a complete set of p2r implementations in the accompanying tables. This may indicate the diversity of options available for developers in the field. For instance, the oneAPI Threading Building Blocks (TBB) library was utilized to ensure that the implementation accurately reflects what was used in the MKFIT project to achieve improved performance.
Regarding the GPU implementation, the basic model was built on a CUDA programming framework, which enables multithreaded programming and iterative processes. One of the most notable benefits provided by this model is allowing each track to be processed by a series of threads in parallel computing. The right to leverage the features of list memories and memory optimizers is crucial, enabling developers to implement robust algorithms on graphical processing units.
The CUDA model offers a high level of abstraction that can be compared to general programming models like OpenCL and SYCL. While it is a model specific to NVIDIA, the code written using it is not necessarily portable across all diverse processors. Therefore, the HIP model for AMD devices was adopted, which aims to achieve portability across different architectures while maintaining many similar foundational rules.
Directive-based solutions, such as OpenMP and OpenACC, are prominent examples of techniques that allow developers to use code annotations to define application properties. These programming models can be incrementally integrated with existing sequential applications, facilitating the transition to parallel versions that leverage advanced processing libraries. From this perspective, adaptive programming tools are now more accessible to developers than ever, enabling them to maximize the utilization of modern hardware resources.
Experiences with TBB and CUDA Libraries
The oneAPI Threading Building Blocks (TBB) library is one of the pivotal tools for implementing parallel operations on multi-core processors, as it provides a simple API that aids in managing and executing threads. Execution threads are organized using clusters or sets of code instructions that are executed in parallel, simplifying the optimization and control of processes. In this context, the use of TBB’s parallel_for loops to cover events and path groups is a significant step towards achieving real performance.
The high performance in TBB execution arises not only from the organization of paths but also from effective methods for parallel processing. Comprehensive monitoring of all tasks distributed across threads ensures proper resource allocation. MKFIT similarly utilizes these solutions to enhance performance in applications that require optimal multi-threaded operations.
In terms of the CUDA model, it is one of the effective models for handling parallel programming, but it focuses more on enhancing performance through graphics processing units. The CUDA library allows each MPTRK to process through a block of threads, which builds on the independence of operations between paths. Transitioning from a TBB implementation to CUDA can highlight the advantages gained from each type of programming.
These tools, as noted, exist in the form of frameworks that provide software developers with flexible capabilities to choose what is most suitable for their specific needs. Each tool has its own strengths and weaknesses, and as development tools advance, these technologies continue to compete with one another, delivering better results and facilitating the transition between different programming models.
Choice Compatibility: Analyzing Different Solutions
Choice compatibility among parallel programming models is a critical aspect of the application development process. With several models available, such as OpenMP, OpenACC, HIP, and CUDA, each option presents its own features that make it suitable for specific purposes. For instance, OpenMP offers the option to provide annotations for tuning parallelism, making it easier for developers to add the necessary abstractions for their CPU-based operations, while OpenACC is similar with a focus on parallel devices.
Transitioning between programming models has become feasible thanks to compatibility techniques that help expedite software development. The importance lies in reducing the need to rewrite large portions of code, simplifying the stringent development process for large projects. Transition experiences between OpenMP and OpenACC provide examples of how to address the challenges associated with switching between models, as parallelism strategies can vary from one to another depending on the compiler’s response.
Experiences with EDA (Electronic Design Automation) transformations demonstrate the feasibility of using such tools, as desired results are achieved with minimal modifications. The greater the flexibility of the adopted solutions, the more efficiently developers can optimize software architectures, opening new avenues for the evolution of custom software tools.
Libraries like Alpaka also commit to compatibility by embracing the concept of adding an abstraction layer to enhance usage across different platforms. All of this points not only to innovation in computational processes but also to providing developers with convenient tools for their development pathways without compromising performance quality.
Usage
Software Libraries for Data Processing
There are multiple software libraries aimed at improving data processing performance, among which are Alpaka and Kokkos. These libraries focus on providing unified solutions targeting high performance across various computing architectures. The example of the Alpaka library is notable, especially in the field of high-energy physics (HEP), where the CMS experiment chose to rely on it as a unified solution to support the use of graphics processing units (GPUs) in LHC Run 3. This choice demonstrates a gradual shift towards using modern and unified technologies due to their role in enhancing productivity and flexibility.
At the same time, the Kokkos library offers similar solutions, based on the concept of adaptable style programming. This library leverages pattern programming techniques to create code that can be executed across multiple platforms, ensuring consistent performance on a variety of computing devices. Kokkos is designed to minimize the complexities arising from programming multiple devices, making it easier for developers to save significant time and resources in data processing.
Furthermore, Kokkos encourages developers to express their algorithms using concepts of general parallel programming, facilitating compatibility with various processing devices. Kokkos also provides specific parallel execution models, giving developers the ability to finely tune execution details to achieve optimal performance.
Standard Parallel Programming using stdpar in C++
The C++ programming language is the preferred choice for implementing high-performance scientific applications, as the recent updates to the ISO C++ standard provided a set of executable algorithms for graphical processors. C++17, for example, includes a wide range of parallel algorithms that extend the Standard Template Library (STL) algorithms and add execution options to aid in adaptation across different computing devices, including multi-core systems and GPUs.
These options, such as std::execution::par and std::execution::unseq, enhance programmers’ ability to achieve improved performance by specifying the execution behavior of algorithms. Meanwhile, libraries like NVIDIA’s nvc++ provide support for offloading stdpar algorithms onto GPUs, allowing for a higher level of integration between CPU and GPU through unified memory management.
However, there are challenges in many ways, as developers must be careful in memory allocation and partitioning between CPU and GPU to ensure that applications execute smoothly without violating memory rules. Experience indicates that developing code using these methods often requires a deep understanding of how devices and memory interact, reflecting the importance of technological knowledge in designing advanced scientific applications.
Programming with SYCL and Its Features
SYCL represents a multi-platform abstraction layer that allows writing code on various processors using standard C++. SYCL was developed to enhance programming efficiency and facilitate access to a range of different computing architectures. One of the notable advantages of SYCL is the ability to use standard C++ code for the central processor and SYCL-specific C++ code for different processors within the same source file, providing an integrated and streamlined development process.
SYCL is designed to be fully compatible with standard C++, allowing developers to use any C++ library in a SYCL application. The SYCL model also focuses on delivering consistent performance across a range of devices, as abstractions are built in a way that allows for high performance without relying on a specific architecture, making SYCL a versatile tool for developers.
Looking at practical examples, practical experiments with SYCL have revealed significant improvements in the performance of computational applications. We conducted a thorough analysis to compare programming methods, and the model used with SYCL resembles the traditional method used with CUDA, facilitating the transition between programming models and enhancing collaboration among different development teams. This also highlights the importance of compatibility among various programming approaches in improving efficiency and the processing speed required in complex research environments.
Measurements
Performance and Practical Applications
One of the main challenges in developing scientific applications is measuring the performance of algorithms. The most important performance metric, from the perspective of HEP computing, reflects the productivity ratio of processing, which determines the number of processes that can be handled per second. This ratio has been measured by applying various techniques across a range of computing systems, including NVIDIA, Intel, and AMD graphics processing units.
During the experiments, practical performance showed that algorithm bundling can lead to significant improvements in productivity ratios. The performance of various tools was tested on a variety of computing systems, where the efficiency of multiple parallel algorithms and their accuracy were assessed. For example, the X-rays used in experiments like MKFIT provided feasible results with different programming techniques, highlighting the importance of optimizing the code and the techniques used.
Analyzing the results provides important insights into how software models can be enhanced to improve performance. For instance, experiments used data collected from several platforms, allowing for a broader understanding of the implications of programming choices and libraries used. This reflects the importance of investment in advanced programming strategies to ensure applications keep pace with rapid technological developments in the field of scientific computing.
Implementation and Performance Analysis Across Different Parallel Libraries
Various parallel libraries are a key tool in optimizing performance in computing operations used on graphics processing units (GPUs). This section discusses the differences between libraries such as CUDA, Alpaka, and Kokkos, and how different systems rely on different compilers to optimize the execution tool. When implementing these libraries, a different compiler is used for each, such as using the nvcc compiler for CUDA libraries, and OpenARC for OpenMP and OpenACC libraries. This diversity in compilers can lead to varying performance outcomes, especially when evaluating kernel performance or overall performance.
During performance evaluation, the same launch parameters are considered, such as the number of blocks and the number of threads per block. In some libraries like Alpaka and Kokkos, these parameters must be set manually, as leaving them unspecified may lead to suboptimal values that significantly reduce performance. Due to the importance of these parameters, the numbers can directly affect the final performance. In some cases, such as setting the number of registers per thread, it was observed that this can affect performance by up to 10%. On the other hand, the stdpar library does not allow for manual specification of these parameters, which may significantly reduce performance effectiveness compared to other libraries.
At different performance values, test examples such as p2z and p2r were set up on various GPUs like V100 and A100, where it appears that the time taken to execute the kernel was determined only by execution times, reflecting the precise performance of these libraries. Generally, various transfer-related solutions led to performance very close to the original CUDA copy performances, except for the stdpar library, which suffered from performance issues due to its reliance on unified memory that requires data transfers for each execution, increasing processing costs.
Impact of Compilers and Software on Performance
Studies have shown a clear dependence on performance based on the compiler used. For instance, in the version relying on OpenACC and OpenMP, performance was tested on the V100 GPU, and the version compiled using OpenARC showed better performance, as this compiler directly followed user settings compared to other compilers such as llvm and gcc. This performance variation is attributed to how each compiler handles user configurations in the source code, where the operating settings in versions compiled using llvm and gcc were lower than those specified, leading to a decline in performance and stress on accessing the global memory.
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For example, in the case of the OpenMP-based version of p2z, data was temporarily allocated in the team’s memory; results were significantly better with the version compiled using OpenARC due to its effective use of shared CUDA memory. In contrast, the versions compiled with llvm and gcc faced issues that included using suboptimal values in setting the number of blocks and threads, adversely affecting performance. Overall, the results indicate that the compiler that closely follows user settings greatly impacts program performance.
When it comes to data transfer, the implementation of memory binding feature showed a significant impact on performance, as binding improves the bandwidth ratio of transfer at the memory level. In the performance of different versions of the application, the optimized versions using binding might show significantly higher performance in data transfer, enabling the programs to operate more efficiently during operations related to libraries like OpenACC and Kokkos.
Performance of Solutions Across Different Architectures
When evaluating performance on other GPU architectures, such as AMD and Intel, support is still less developed compared to NVIDIA graphics cards, although this area has seen rapid expansion. The performance of the p2r version of the application was developed for testing on different GPUs; however, the results indicate that overall performance still requires some improvements. Each specialized application requires specific optimizations to make it more efficient on different visual processing units.
The performance of libraries such as HIP (for AMD units) and SYCL (for Intel units) was compared with the native performance of each. Measurements showed that performance was acceptable, but more effort is needed to improve compatibility and revert to normal performance. For example, performance was measured on the MI-100 and A770 units. As certain libraries are optimized to operate efficiently across these architectures, overall performance improved compared to traditional execution.
Some performance observations include that you may not always achieve optimal performance without adjusting the correct settings and improvements, in addition to the importance of bandwidth and data transfer time that should be taken into consideration when measuring performance. The potential results reflect the inability of current solutions to compete with more mature libraries like CUDA on NVIDIA GPUs. Therefore, improving performance integration and designing libraries for AMD and Intel leaves multiple requirements that must be met to reach a performance level close to what NVIDIA offers.
Analysis of AMD and Intel GPU Architectures
The GPU architectures of AMD and Intel are among the available solutions to improve performance in complex applications, such as path reconstruction applications for charged objects. The discussion addresses how to use modern transport tools like HIP, Alpaka, and Kokkos to achieve reasonable performance across different GPU platforms. Testing conducted on the JLSE platform, which features AMD EPYC processors and MI100 units, shows that transitioning between different environments, such as CUDA and HIP, occurs smoothly without the need for code changes. For example, the equivalent version of HIP in Alpaka showcased better performance than the version derived from CUDA, while Kokkos performance was close to that, limited by an approximate factor of 2.
Results indicate that despite the challenges, performance efficiency testing on Intel GPUs, such as the A770, showed significantly lower performance figures due to reliance on single precision floating-point operations. By reducing the precision of operations to single, performance was improved; however, reliance on double precision instead can lead to significant slowdowns ranging from 3 to 30 times. It is worth noting that tools like SYCL in Alpaka are still in experimental support stages, and the development of Kokkos is still active, emphasizing the importance of using the latest versions of tools for achieving noticeable performance improvements.
Performance
CPU in Application Execution
The MKFIT application used Threading Building Blocks (TBB) library as a benchmark for CPU-level performance. The discussion states that the original execution relied on an outdated version of the Intel C++ Compiler, which led to performance improvements of up to 2.7 times in execution time. However, due to the lack of support for this version, it was chosen not to include it in the main results. Data regarding the performance of the fast models p2z and p2r on a dual-socket system equipped with an Intel Xeon Gold 6248 chip show that all were compiled using gcc, positively reflecting comparative performance with the original TBB implementation.
The Alpaka implementation of the p2z benchmark managed to surpass the reference TBB implementation, reflecting Alpaka’s ability to utilize memory allocation more efficiently. Multi-threading and vectorized instructions had significant effects on performance, where best practices required optimizing data layout and ensuring that loops were properly designed for vectorized processing during execution. The performance related to SYCL in the p2r model was controversial, achieving only 27% of the reference execution performance, highlighting the challenges many developers face when switching to new languages.
Challenges of Performance Optimization in Diverse Applications
Efforts to migrate performance-driven applications from CPU to GPU faced multiple challenges. When optimizing competition among tools, it was found that memory patterns and advanced allocation had a significant impact on final performance. Performance improvements of up to six times were reported in some cases by optimizing how data is stored in memory and leveraging memory prepinning for NVIDIA GPUs. Meanwhile, the choice of compiler sometimes significantly affected the throughput performance of different units.
Continuous updates to tools and libraries are a crucial necessity to ensure performance is maintained, as experiments showed a noticeable improvement in Intel GPU performance when updated to newer versions of the Kokkos library. Experiments are varied as workflows that were effective on certain types of processors or units may not necessarily be effective on others. This dynamic underscores the importance of diversity in tools and their specialization, helping to provide suitable solutions that meet the increasing demands of data analysis applications in high-energy physics experiments.
Future Development Opportunities in Performance Tools
Future advancements in the computing world are trending towards improving application execution across different processing units while enhancing portability among different units. Current tools, such as Alpaka, Kokkos, and SYCL packages, provide opportunities to reuse legacy codes across new infrastructure without needing to rewrite the entire application code. However, achieving the desired performance remains a challenge that requires investment of time and effort into optimizing processes and appropriately organizing data.
Collaboration among diverse resources, from research laboratories to educational institutions, is necessary to support research related to high-energy physics utilizing these advanced technologies. Providing access to data and applications is a significant step towards achieving these goals, which requires building an effective community of developers addressing performance and efficiency issues. As the pressure to develop advanced analytical techniques increases, it is crucial to continue exploring new performance and portability options to succeed in these ambitious projects.
Data Processing Challenges in High Energy Physics
Modern experiments in high-energy physics require processing vast amounts of data while searching for extremely rare interactions between fundamental particles. For example, the CMS experiment at the Large Hadron Collider (LHC) at CERN processed hundreds of petabytes of detector and Monte Carlo simulation data during the second run of the collider (2015-2018). Data rates are expected to increase significantly in experiments like the High Luminosity LHC and DUNE in the coming decade. The high-energy physics sector faces additional challenges with the expected event rate increasing by a factor of 7.5, meaning data volumes will reach exabyte limits. Therefore, dealing with these large volumes of data requires significant shifts in conventional computing methods and an advanced vision for the effective use of computational resources.
Necessity
Parallel and Versatile Programming
The need for parallel and versatile programming is increasing within the framework of modern high-energy physics experiments. Experiments in the past primarily relied on traditional Central Processing Units (CPUs), but things are changing with the shift towards using Graphics Processing Units (GPUs) that offer improved performance. The way forward is to extend existing software ideas to take advantage of the diverse architecture provided by High-Performance Computing (HPC) centers. Utilizing multi-programming methods such as “Kokkos” or “Alpaka” helps researchers prepare their programs to tackle the new challenges posed by “Exascale Computing.” By restructuring programs to be compatible with these new resources, performance can be enhanced and maximum computing power can be leveraged.
The Shift Towards Distributed Computing
Distributed computing contributes to enhancing the ability to process massive amounts of data through large networks of interconnected computing centers. The Worldwide LHC Computing Grid (WLCG) model is an example of how multiple centers in various countries collaborate to process data efficiently. However, the need will increase and become more complex due to the growing complexity and volume of data in the future. This will require the development of new models to manage the multiple machines and the necessity of making algorithms capable of working efficiently across this distributed environment.
Software Development Strategies
Enhancing the ability to process data from high-energy physics experiments requires the development of new strategies in software programming. Developing algorithms to be scalable and leveraging versatile resources such as GPUs is a priority. Additionally, software must be flexible enough to keep pace with potential technological shifts in the coming years. By creating a framework that can easily adapt to future infrastructures, scientists will be able to enhance analytics performance and extract deeper insights from the data.
Innovation in Programming Applications
Innovation in programming applications requires creative flexibility to handle large data from high-energy physics research. Different applications such as “Parallel Kalman Filter” offer new models for analyzing spatial and particle data, aiding in accelerating and improving performance. By measuring optimal performance, researchers can predict how well these new technologies will fit with the upcoming massive computing systems. Continuous innovation is what will enable experiments to cope with the vast amount of data and make the most of the precise examination of tiny particles.
Future Directions and Remaining Challenges
Despite significant advancements in computing strategies for high-energy physics, there are remaining challenges that require ongoing attention. The evolving requirements for partitioning and storage may demand more unconventional solutions to ensure that big data can be processed efficiently and securely. These challenges range from the need to create new models of processing and storage to achieving fast communications between systems. Maintaining a substantial portion of knowledge and technological advancement is key to ensuring a promising future in scientific research. The success of these challenges lies in the ability to assimilate rapid technological shifts and employ them creatively to enhance the capabilities of modern experiments in this sensitive field.
Challenges in Data Processing in Particle Physics Experiments
Data processing from experiments in the field of High-Energy Physics (HEP) is a complex process that involves several sequential steps. Starting from data collection, then reconstructing the raw data into higher-level information, leading to final data analysis and statistical interpretations. The main difficulty in this process lies in the volume of data that needs to be processed and the complexity of the computational operations necessary to understand the events. For example, in the proton collision experiment at the Large Hadron Collider (LHC), analyzing each event requires extracting precise information about the trajectories of charged particles. This involves using advanced algorithms such as the Kalman filter algorithm, where the position and velocity of particles are inferred from the available information.
The challenges
technology leads to the urgent need to develop interactive and effective algorithms that can be implemented on diverse computing platforms. These challenges are not limited to performance alone, but also include the convenience of using the programming language, which requires developers to rewrite code to enable compatibility with multiple platforms. This places an additional burden on data scientists who face time pressures in analyzing the large amounts of data generated from experiments.
Performance Improvement in HEP Algorithms
The main goal of efforts aimed at improving the performance of HEP algorithms is to achieve maximum efficiency in data processing. This is accomplished by accelerating computational processes through techniques such as parallel processing and employing specialized processors like Graphics Processing Units (GPUs). The MKFIT program is a successful example of optimization, as it was designed to rewrite traditional particle tracking algorithms with the aim of significantly speeding up performance. According to experiments, MKFIT can achieve improvements of up to six times compared to the previous implementations in particle tracking.
MKFIT utilizes techniques such as sequential data storage to enhance access during computational processes, allowing SIMD (Single Instruction, Multiple Data) operations to be executed quickly and efficiently. The focus is on exploiting electrical data structures that permit complex calculations to be conducted faster. These improvements are not limited to algorithm performance alone but also relate to reducing the time required to analyze each event, enabling scientists to obtain results in a timely manner.
Code Transfer Tools and Portable Solutions
Code transfer tools and portable solutions are modern necessities in the field of scientific programming, especially when it comes to developing HEP algorithms. In the past, programs were written in specialized languages, such as CUDA, limiting their applicability to specific platforms. However, creating code that enhances compatibility with multiple platforms has become a vital reality today. Several solutions have been developed, such as HIP, Kokkos, and SYCL, all aimed at facilitating the code transfer process across different environments.
One of the key goals in developing these tools is the capability to write a single source code that can be compiled and executed on multiple platforms. This not only saves effort and time in maintenance but also enhances the ability to share knowledge and algorithms among different research teams. For example, the integration of tools like OpenMP and OpenACC enhances the ability to manage parallel behaviors and allocate memory effectively. It is worth noting that all these solutions rely on open standards, but they require specific software packages to operate on certain GPU units.
Performance Tests and Evaluation
Performance evaluation in high-performance software is an integral part of the software development process. This requires conducting precise tests to measure how efficiently the software processes its functions under various conditions. In the context of this work, independent testing algorithms were used to assess the effectiveness of different solutions in code transfer. This involved measuring the performance of traditional algorithms against reverse solutions using various tools and providing a comprehensive user experience.
Performance results represent vital aspects of achieving a comprehensive understanding of the competitiveness of software. By measuring quantitative performance values and user experience, the extracted data provides insights into how to improve the solutions. Additionally, these experiments contribute to identifying aspects that require continuous improvement, whether in computational performance or programming ease of use. These experiments conclude with numerous lessons learned on how to enhance software performance and portable solutions in the field of HEP.
Particle Tracking and Experimental Measurements
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This requires a complex process involving testing multiple methods to assemble the hits. This can be achieved, for example, through the Kalman algorithm, which is a mathematical model used to estimate uncertain states from a set of measurements. The Kalman algorithm relies on a prediction step, where the conditions of the path are defined based on a known previous set, followed by an update step that takes new hits into consideration. By improving the accuracy in determining locations, researchers can arrive at more precise results relating to the initial interactions of particles.
Structural Layout of Detection Devices
Particle detection devices in major experiments, such as CMS and ATLAS, are divided into two main sections: the “barrel” representing the cylindrical part parallel to the beam pipe, and the “end-cap” extending on either side of the barrel. This design is used to enhance the system’s ability to efficiently detect charged particles. The motion measurements of charged particles in a steady magnetic field depend on a helical path, allowing researchers to calculate their locations accurately.
Each layer of the detection devices has different characteristics, allowing for the accurate capture of information about motion and the systematic study of impact points. This level of organization leads to effective information exchange between various system elements and helps estimate the particle’s motion over the data flow.
Analyses and Computational Steps
Depending on the input data, the initial step in the pathfinding algorithm is to create “path seeds,” which represent a series of initial guesses about the state. These seeds are usually built using a set of hits taken from the inner layers of the detection device. Processing operations involve intensive calculations requiring numerical processing, including leveraging mathematical properties like trigonometric functions and matrix operations.
The computational operations aim to improve the overall performance of the system. These operations are formed within a data model known as the “compact matrix structure of structured numbers,” which enables faster and easier access to data, enhancing the effectiveness of data processing operations. If executed correctly, execution time can be reduced, and productivity can be doubled.
Implementing Algorithms and Software Tools
Researching tools and implementation mechanisms reflects the importance of innovation in data processing. A variety of analysis tools have been tested on multiple systems, but not all possible scenarios could be tested. Most of the methods used rely on advanced software libraries, such as multithreaded libraries that provide improved performance when handling large datasets.
Programming libraries like TBB and CUDA help enhance the effectiveness of implementing pathfinding algorithms by organizing threads and managing tasks for optimal efficiency. These libraries have been used in CMS experiments to streamline large complex applications, contributing to faster mathematical calculations and enhancing the system’s ability to handle data more effectively and accurately.
Performance Improvement through Analysis and Evaluation
The process of analysis and evaluation is considered one of the critical tasks to ensure accuracy and speed of performance in the particle detection system. These processes require continuous monitoring of measurements and the results generated from various models. When improvements are executed appropriately, high performance can be achieved by using a mix of traditional and modern programming techniques.
The challenge
The core is to achieve a balance between the complexity of models and processing speed. Through practical tests and multiple criteria, it can be ensured that the techniques used operate efficiently across various platforms. The appropriate choice of software tools and structure optimization distinguish each application individually, aiding researchers and experts in accurately achieving their goals.
Memory Access Efficiency and Shared Memory Utilization
The parallel programming for different devices such as Graphics Processing Units (GPUs) is characterized by challenges related to memory access efficiency. Through analyzing how data is processed, it has been determined that retaining intermediate results in local registers can provide more efficient memory access. Optimal memory access is a key factor in performance enhancement, as inefficient paths lead to excess momentum in execution time. By using shared memory, it is assumed that everyone in the processing block can access it to improve productivity, but studies have shown that this option may have a counterproductive outcome in some cases.
In the context of the different technologies used in parallel programming, CUDA is an example of how specific features of NVIDIA hardware can be leveraged to enhance performance. Although CUDA provides the capability to exploit specific properties of NVIDIA devices, the developer must deal with portability barriers when using this platform. This means that code written using CUDA may require significant modifications to function correctly on different devices.
In conjunction with these challenges, AMD introduced the HIP programming model, which focuses on portability between NVIDIA and AMD devices. This model demonstrates how to alleviate portability barriers through a CUDA-like design, facilitating the process of writing and transforming code between different base conventions. This transition shows the increasing importance of compatibility between multiple systems and the ability to exploit the specific features of each architecture.
Directive-Based Models: OpenMP and OpenACC
High-level directive-based models such as OpenMP and OpenACC are powerful tools for developers seeking to convert sequential applications written in C, C++, or Fortran into parallel versions. The working mechanism of these models relies on using specific directives that the code compiler can understand, allowing developers to specify application characteristics such as available parallelism and data sharing rules.
The main advantages of directive-based programming models lie in the ability to progressively transition existing applications to parallel versions without requiring drastic changes to the current software architecture. This capability facilitates adaptation to the rapid computing needs of multiple platforms. For example, the initial version of OpenMP was designed by transforming the reference CPU implementation of TBB into OpenMP implementation, demonstrating the smoothness of possible transitions between languages.
Strategies related to parallelism and directives differ between the versions targeting devices. For example, when targeting a GPU, significant OpenMP directives become crucial for performance close to that of the patents used in processing, whereas in CPU systems, these directives may become unnecessary. Transformations between OpenMP and OpenACC can pose challenges due to differences in mapping strategies and the variety of configurations supported by different compilers, which can also affect final performance.
Transitioning to Programming Libraries like Alpaka and Kokkos
Libraries such as Alpaka and Kokkos provide new pathways to facilitate parallel application programming. Alpaka is a library that relies on the concept of “single source” and has a similar approach to CUDA at the API level. Alpaka simplifies it for programmers to achieve portability by adding an abstraction layer between applications and device-based programming models, allowing for more effective code writing across different devices.
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Another aspect, Kokkos emerges as a library based on meta-template programming, allowing for device-independent code generation. Kokkos provides a set of concepts and principles that require developers to express algorithms in a general manner before they are automatically mapped to the specific processing device. This separates the concept of writing device-dependent code from adhering to modern C++ standards.
Kokkos and Alpaka can be excellent options for those in scientific research, as they offer solutions that deliver performance close to native performance when optimizations are applied correctly. Applications used in physical science experiments, such as CMS experiments, use Alpaka as a reliable solution to support transport in GPU utilization, facilitating the integration of performance and programming efficiency.
Standard Parallelism Using stdpar in C++
The programming language C++ is the preferred choice for many high-performance scientific applications. Recent updates to the ISO C++ standard have introduced a set of algorithms capable of working on multi-device architectures. The use of stdpar, introduced in the C++17 standard, provides a balance between code productivity and computational efficiency.
stdpar allows developers to specify the expected degree of parallelism in new algorithms. The general-purpose algorithms available in the Standard Template Library (STL) are designed to be effective across current multi-threaded architectures, making it easier to apply these techniques in commercial applications. This update is a testament to the trend toward performance enhancement by enabling algorithms to be executed in parallel.
C++17 provides execution policies like std::execution::par and std::execution::par_unseq, which offer advanced execution patterns. These policies provide developers with the flexibility to write code that is more compatible with modern hardware. However, transferring algorithmic code between different systems requires efficiency and alignment of elements to avoid errors due to unmanaged assignments.
Memory Management in GPU and CPU Programming
Memory management constitutes one of the critical issues in programming systems with multiple processors, where developing code that blends CPU and GPU usages requires precise strategies to optimize performance and avoid memory violations. Using pointers to CPU stack or general objects in GPU code can lead to memory breaches, highlighting the importance of careful memory management in the programming process. The methodology that combines the usage of nvc++ in multi-processor environments is based on a strict approach to memory handling, reflecting the importance of careful handling of allocation and referencing. Although developing code in these applications is quite similar to standard C++ programming, the main difference lies in observing the previously mentioned constraints.
It requires developers to have a comprehensive understanding of how to allocate and manage memory effectively. A well-known example of the problems that can arise when pointers are misused is referencing a freed memory location, leading to unpredictable behavior. Thus, developers should adopt precise programming techniques and have sufficient knowledge of the complex mechanisms followed by GPU processors to execute operations.
SYCL: A Cross-Platform Abstraction Layer
SYCL is a standard developed to facilitate writing portable code across multiple processors in a “single-source” manner, using standard C++. The Khronos group promotes this standard, however, the primary focus on performance optimization is currently being spearheaded by Intel. One of the main advantages of SYCL is its ability to handle regular CPU C++ code alongside a dedicated GPU portion within the same source file, greatly simplifying the development process.
Using SYCL offers a unique pathway toward integrated programming, allowing developers to utilize various C++ libraries in SYCL applications. This reflects the flexibility of SYCL as a distinctive programming tool. There is a significant emphasis on the performance applicability of the application across a wide range of hardware architectures, allowing for performance optimization of applications without being tied to a specific architecture or particular core language.
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The issues that may arise when using SYCL are how to effectively manage memory. The Unified Shared Memory feature is used for data management, and this requires developers to be aware of how to work efficiently with memory management to avoid delays in data transfer between the CPU and GPU. The success of applications using SYCL largely depends on the balance between performance and resource management.
Software Performance Results on Various Systems
In the field of simulating responses to physical events, the ability to process a large number of pathways per second is the primary performance metric. Performance was measured by executing approximately 800,000 pathways on a single core, where several software systems were developed to process data and evaluate performance. Performance was tested on multiple systems including NVIDIA and AMD graphics processing units and Intel central processing units.
Results show that most different performance compatibility solutions achieved results close to native performance. Some SYCL-based versions were the lowest performing, as in-depth analysis revealed a significant branch when using SYCL, indicating challenges that may require a more comprehensive understanding of performance impacts. Data performance is often superior due to the integration of memory management and how effectively it is handled.
The effects of using various compilers were studied, and performance results showed significant differences among them, highlighting that the correct choice of tools can have a substantial impact on final outcomes and execution time. It is recognized here that results can be influenced by external factors such as hardware architecture settings and scalability, which requires considerable attention from developers.
Parallel Performance in OpenMP Implementation
The performance of OpenMP applications such as p2z primarily depends on how memory is managed and parallel execution. In versions compiled using llvm/gcc/IBM, performance faced significant challenges concerning inadequate memory management compared to OpenACC versions. For example, the version compiled using OpenARC manages temporary data more efficiently by allocating space in CUDA shared memory. In contrast, the versions compiled using llvm/gcc/IBM relied more on global memory, adding more load due to repeated memory accesses. These accesses or transitions between memory types represented a significant portion of execution time, contributing to the lower performance of these versions.
To illustrate, when compared using a V100 GPU, performance measurements across different versions showed varied results. In OpenACC versions, OpenARC achieved better data transfer performance compared to versions using nvc++. The difference lies in how each compiler handles data transfer calls. For instance, while OpenARC converts each item in the data transfer list to a single transfer call, nvc++ splits the data transfer into smaller, multiple calls, resulting in poorer performance in this case.
Therefore, it can be said that the execution environment and compilation tools play a crucial role in optimizing parallel performance. Performance enhancement is a vital element, and deep consideration is required in how data is managed across memory and the appropriate compilers used.
The Impact of Memory Pinning on Performance
Host memory pinning is an extremely important concept in optimizing GPU application performance. Memory pinning allows for direct memory access (DMA), providing better bandwidth compared to non-DMA transfers. In the context of the Kokkos and OpenACC version of p2z, implementing memory pinning had a significant impact on performance. For instance, when memory pinning was enabled, transfer performance improved dramatically, and the timing overhead was significantly reduced.
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The comparison was applied to several versions of OpenACC applications, where the results showed that the version using shared memory with concurrent transfer provides lower performance than the version relying on thread-private data in local memory. Therefore, it became clear that relying on well-defined graphs and exploiting memory pinning capabilities is essential for the desire to improve performance.
When examining the experiments with other GPUs, such as AMD and Intel, it was found that good practices in memory pinning can significantly enhance performance. For example, performance tests with AMD MI-100 showed a substantial speed improvement thanks to memory pinning, reflecting how cross-architecture optimization can enhance various programming environments.
Performance Results of AMD and Intel GPUs
The support for GPU environments from AMD and Intel is characterized by challenges compared to NVIDIA, but there has been notable progress over time. Through the initial performance of p2r on both AMD and Intel, a significant difference in performance results was observed. On the AMD GPU, frameworks like Kokkos and Alpaka performed well despite no dedicated efforts to optimize performance on those systems. This indicates the potential for easy transition between frameworks without the need for major changes in the code.
When considering throughput measurements on Intel A770, the results were lower due to it being a GPU not designed for high performance. It was also found that relying on double-precision operations leads to performance degradation by up to 30 times, emphasizing the need to make use of implicit modifications that allow dealing with low-precision data to make application execution efficient.
The results underscore the importance of selecting the appropriate environment and specific programming needs, helping developers closely leverage the available tools to improve performance and expand support for different systems. Here, the continuous progress in supporting other systems and the need to engage broader communities to ensure performance improvement over time emerges.
Central Processing Unit (CPU) Performance
In terms of central processors, using Intel’s Threading Building Blocks (TBB) library is a key tool for improving performance in applications like MKFIT. The results were mixed, with some versions of applications achieving good performance compared to the original versions, especially when using the appropriate TBB technology. However, past complexities were presented when the original version was developed using an old compiler that is no longer supported, which affected the final delivery. It is worth noting that the extended versions of p2z were able to achieve performance equivalent to more than 70% of the original performance using TBB.
Matters related to optimization require careful planning, especially concerning the arrangement of data structures and appropriate preferences, to effectively leverage the triad of multi-processing, hard drives, and memory as efficiently as possible. The performance of Alpaka-specific versions was outstanding, serving as an example not only of efficiency but also of the necessity for diversity in increasing performance in a manageable manner over time.
The efforts put into performance development require frequent review and comprehensive analysis to identify the optimal path for dealing with challenges while the system runs advanced algorithms. These experiences provide valuable insights into how libraries interact with the internal architecture of the system in the contexts discussed.
Rediscovering Performance in Computing
Rediscovering performance in computing is considered one of the vital topics that need to be addressed with a deep understanding of the challenges associated with different types of processors and parallel computing systems. With the growing use of GPU processors, especially of the NVIDIA type, it has become evident that the steps taken to enhance performance may yield varying results when applied to other types of processors like AMD and Intel. This requires the development of new strategies to ensure that performance improvements on a specific architecture can translate into gains on another architecture.
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This process revealed multiple factors significantly affecting overall performance, such as memory layout and explicit memory prepinning. For example, it was observed that optimizing memory layout can lead to a speedup of up to six times in some applications. Performance improvements in high-energy computing environments depend on several variables, including the choice of the appropriate compiler, as the active evolution of these tools means that keeping performance updated with newer releases becomes crucial. The use of libraries like Kokkos seems to help achieve a noticeable performance increase, as evidenced when updating the Intel GPU library resulted in a doubling of performance.
All these factors emphasize the importance of rethinking traditional approaches and developing new solutions for portability across different systems. Regarding performance, testing specific libraries and applications on various architectures is essential for achieving optimal performance. Some programming libraries like Alpaka, Kokkos, and SYCL offer portable solutions, but many require significant optimization for good performance. Thus, the ability to execute algorithms on different processors will allow important physics experiments like HEP to benefit from diverse computing resources.
The Importance of Developing Portable Solutions
The ability to run algorithms on processors from different vendors is a fundamental element of success in physics experiments like HEP. These technologies provide a significant advantage in leveraging available computing resources, including those in current and planned centers. Providing robust computing systems means we can better handle data analyses that require processing vast amounts of data. Many HEP applications need continuous performance optimization to respond to the increasing challenges posed by complex big data.
Moreover, developing tools and software that allow the reuse of existing algorithms on new platforms requires ongoing effort. The transition from central processing units (CPUs) to graphical processing units (GPUs) is not straightforward, nor is it expected to achieve equal performance without precise adjustments. Therefore, periodic tests must be conducted to ensure that all software updates enhance performance rather than degrade it. Also, advancements in libraries like OpenMP and OpenACC open new horizons for researchers, but technical knowledge is still required to improve how these libraries are applied to build portable solutions.
Furthermore, the speed of data processing requires periodic re-examination of programming methods and ensuring that planned solutions can adapt to changes in infrastructure and technological innovations. Efficiency in complex calculations will only be realized when developers and researchers collaborate in enhancing software and sharing knowledge about the most effective methods.
Future Challenges in Developing Software for High-Energy Physics
While significant progress has been made in the area of algorithm portability, many challenges still lie ahead for the scientific community to ensure the sustainability of these efforts. Researchers today face hurdles related to the complexities associated with multiprocessor systems and ever-changing technology. Increased processing power also necessitates addressing new issues that may arise, such as compatibility and reliability among different libraries and performance variability across diverse computing systems.
By focusing on the continuous development of tools and software, alternative and innovative ways to sustainably enhance performance must be explored. Research into deep learning and artificial intelligence tools may provide something beneficial. Utilizing new techniques in data analysis can contribute to improving responsiveness to the needs of ambitious projects like those related to HEP, requiring flexibility in the strategies employed.
Collaboration
International collaboration is also of great importance, as various research centers can share knowledge and technologies, contributing to the development of affordable and effective solutions. Therefore, it is essential to consider the adjustment and organization of software to be accessible and usable across different platforms worldwide. Efforts to improve the methodologies used will have a significant impact on the quality of research in the future.
Source link: https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2024.1485344/full
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