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Adaptive Per-File Lossless Compression of Floating-Point Data

Adaptive Per-File Lossless Compression of Floating-Point Dat...

引用

1st international Conference on Smart Energy Systems and Artificial Intelligence (SESAI)

作者： Rodriguez, Andrew Azami, Noushin Burtscher, Martin Texas State Univ Dept Comp Sci San Marcos TX 78666 USA

ISBN: (纸本)9798350364613;9798350364606

The large amount of floating-point data generated by scientific applications makes data compression essential for 110 performance and efficient storage. However, floating-point data is difficult to compress losslessly, and most compression algorithms are only effective on some files. In this paper, we study the benefit of compressing each file with a potentially different algorithm. For this purpose, we created AdaptiveFC, which is based on a tool that can chain data transformations together to generate millions of compression algorithms. AdaptiveFC uses a genetic algorittm to quickly identify an effective compressor in this vast search space for a given file. A comparison of AdaptiveFC to 15 leading lossless CPU compressors on 77 files from 6 datasets in the STAIDench suite shows that per-file compression yields higher compression ratios on average than any individual algorithm.

关键词： data compression floating-point data genetic algorithm CPU processing

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Benchmarking variables for checkpointing in HPC applications

Benchmarking variables for checkpointing in HPC Applications

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1st international Conference on Smart Energy Systems and Artificial Intelligence (SESAI)

作者： Fu, Xiang Huang, Xin Xu, Wubiao Zhang, Weiping Meng, Shiman Guo, Luanzheng Sato, Kento Nanchang Hangkong Univ Nanchang Jiangxi Peoples R China Pacific Northwest Natl Lab Richland WA 99352 USA RIKEN R CCS Tokyo Japan

ISBN: (纸本)9798350364613;9798350364606

Checkpoint/Restart (C/R) is a widely used fault tolerance mechanism in converged systems of cloud, edge, and HPC. However, users often rely on their experience to determine which variables to checkpoint, as there is currently no benchmark that can provide a reference. This can result in checkpointing redundant or even incorrect variables. To address this issue, we propose a benchmark suite that includes critical variables for checkpointing, which have been manually identified, and a method for identifying those critical variables, with 20 representative HPC applications. Our method involves analyzing data dependency between variables to identify critical variables analytically. We verify the identified variables' correctness with a widely used C/R library FTI by an ablation study. With our benchmark suite and data dependency analysis, HPC practitioners now have a reference for identifying checkpointing variables and better knowledge of what kind of variables to checkpoint.

关键词： Benchmarks Checkpoint/Restart Fault tolerance HPC resilience

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Introducing parallel Computing in a Second CS Course 36

Introducing Parallel Computing in a Second CS Course

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36th ieee international parallel and distributed processing symposium (ieee IPDPS)

作者： Newhall, Tia Webb, Kevin C. Chaganti, Vasanta Danner, Andrew Swarthmore Coll Comp Sci Dept Swarthmore PA 19081 USA

ISBN: (纸本)9781665497473

The ubiquity of multicore processors, cloud computing, and hardware accelerators have elevated parallel and distributed computing (PDC) topics into fundamental building blocks of the undergraduate CS curriculum. Therefore, it is increasingly important for students to learn a common core of introductory PDC topics and develop parallel thinking skills early in their CS studies. We present the curricular design, pedagogy, and goals of an introductory-level course on computer systems that introduces parallel computing to students who have only a CS1 background. Our course focuses on three curricular goals that serve to integrate the ACM-ieee TCPP guidelines throughout: a vertical slice through the computer of how it runs a program;evaluating system costs associated with running a program;and taking advantage of the power of parallel computing. We elaborate on the goals and details of our course's key modules, and we discuss our pedagogical approach that includes active-learning techniques. We find that the PDC foundation gained through early exposure in this course helps students gain confidence in their ability to expand and apply their understanding of PDC concepts throughout their CS education.

关键词： parallel and distributed computing CS Education curriculum design

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Resource-Limited Localized Adaptive Adversarial Training for Machine Learning Model 21

Resource-Limited Localized Adaptive Adversarial Training for...

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21st ieee international symposium on parallel and distributed processing with applications, 13th ieee international Conference on Big Data and Cloud Computing, 16th ieee international Conference on Social Computing and Networking and 13th international Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Rajhi, Mohammed Pissinou, Niki Florida International University School of Computing and Information Sciences MiamiFL United States Jazan University Faculty of Computer Science & Information Technology Jazan Saudi Arabia

ISBN: (纸本)9798350329223

Balancing robustness and computational efficiency in machine learning models is challenging, especially in settings with limited resources like mobile and IoT devices. This study introduces Adaptive and Localized Adversarial Training (ALAT), an optimization approach that balances these competing needs. ALAT combines generalized models with localized adversarial perturbations and adaptive data augmentation. As a result, the model strengthens its weak points without needing to explore all possible adversarial threats, saving computational effort Our data shows that ALAT-trained models perform robustly with less computational cost compared to traditional adversarial training methods. This adaptability makes ALAT suitable for various machine learning architectures and particularly valuable in resource-constrained settings requiring resilience to adversarial threats. © 2023 ieee.

关键词： Computational efficiency

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Integrating interactive performance analysis in Jupyter Notebooks for parallel programming education

Integrating interactive performance analysis in Jupyter Note...

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1st international Conference on Smart Energy Systems and Artificial Intelligence (SESAI)

作者： Oden, Lena Noelp, Klaus Brauner, Philipp Univ Hagen Comp Engn Hagen Germany Rhein Westfal TH Aachen Human Comp Interact Ctr Aachen Germany

ISBN: (纸本)9798350364613;9798350364606

Understanding the performance behavior of parallel applications is important in many ways, but doing so is not easy. Most open source analysis tools are written for the command line. We are building on these proven tools to provide an interactive performance analysis experience within Jupyter Notebooks when developing parallel code with MPI, OpenMP, or both. Our solution makes it possible to measure the execution time, perform profiling and tracing, and visualize the results within the notebooks. For ease of use, it provides both a graphical JupyterLab extension and a C++ API. The JupyterLab extension shows a dialog where the user can select the type of analysis and its parameters. Internally, this tool uses Score -P, Scalasca, and Cube to generate profiling and tracing data. This tight integration gives students easy access to profiling tools and helps them better understand concepts such as benchmarking, scalability and performance bottlenecks. In addition to the technical development, the article presents hands-on exercises from our well-established parallel programming course. We conclude with a qualitative and quantitative evaluation with 19 students, which shows a positive effect of the tools on the students' perceived competence.

关键词： Jupyter parallel programming performance analysis interactive programming high perfonnance computing

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Optimizing Huffman Decoding for Error-Bounded Lossy Compression on GPUs 36

Optimizing Huffman Decoding for Error-Bounded Lossy Compress...

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36th ieee international parallel and distributed processing symposium (ieee IPDPS)

作者： Rivera, Cody Di, Sheng Tian, Jiannan Yu, Xiaodong Tao, Dingwen Cappello, Franck Univ Alabama Dept Comp Sci Tuscaloosa AL 35487 USA Washington State Univ Sch Elect Engn & Comp Sci Pullman WA 99164 USA Argonne Natl Lab Math & Comp Sci Div Lemont IL USA

ISBN: (纸本)9781665481069

More and more HPC applications require fast and effective compression techniques to handle large volumes of data in storage and transmission. Not only do these applications need to compress the data effectively during simulation, but they also need to perform decompression efficiently for post hoc analysis. SZ is an error-bounded lossy compressor for scientific data, and cuSZ is a version of SZ designed to take advantage of the GPU's power. At present, cuSZ's compression performance has been optimized significantly while its decompression still suffers considerably lower performance because of its sophisticated loss-less compression step-a customized Huffman decoding. In this work, we aim to significantly improve the Huffman decoding performance for cuSZ, thus improving the overall decompression performance in turn. To this end, we first investigate two state-of-the-art GPU Huffman decoders in depth. Then, we propose a deep architectural optimization for both algorithms. Specifically, we take full advantage of CUDA GPU architectures by using shared memory on decoding/writing phases, online tuning the amount of shared memory to use, improving memory access patterns, and reducing warp divergence. Finally, we evaluate our optimized decoders on an Nvidia V100 GPU using eight representative scientific datasets. Our new decoding solution obtains an average speedup of 3.64x over cuSZ's Huffman decoder and improves its overall decompression performance by 2.43x on average.

关键词： distributed processing Analytical models Memory management Graphics processing units distributed databases Throughput Data models

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parallel Optimization for Robotics: An Undergraduate Introduction to GPU parallel Programming and Numerical Optimization Research

Parallel Optimization for Robotics: An Undergraduate Introdu...

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1st international Conference on Smart Energy Systems and Artificial Intelligence (SESAI)

作者： Plancher, Brian Columbia Univ Barnard Coll New York NY 10027 USA

ISBN: (纸本)9798350364613;9798350364606

While parallel programming, particularly on graphics processing units (GPUs), and numerical optimization hold immense potential to tackle real-world computational challenges across disciplines, their inherent complexity and technical demands often act as daunting barriers to entry. This, unfortunately, limits accessibility and diversity within these crucial areas of computer science. To combat this challenge and ignite excitement among undergraduate learners, we developed an application-driven course, harnessing robotics as a lens to demystify the intricacies of these topics making them tangible and engaging. Our course's prerequisites are limited to the required undergraduate introductory core curriculum, opening doors for a wider range of students. Our course also features a large final-project component to connect theoretical learning to applied practice. In our first offering of the course we attracted 27 students without prior experience in these topics and found that an overwhelming majority of the students fell that they learned both technical and soft skills such that they felt prepared for future study in these fields.

关键词： CS Education GPU Programming Numerical Optimization parallel Computing Robotics Undergraduate Education

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Locality-aware Qubit Routing for the Grid Architecture 36

Locality-aware Qubit Routing for the Grid Architecture

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36th ieee international parallel and distributed processing symposium (ieee IPDPS)

作者： Banerjee, Avah Liang, Xin Tohid, R. Missouri S&T Dept Comp Sci Rolla MO 65409 USA Louisiana State Univ Ctr Computat & Technol Baton Rouge LA 70803 USA

ISBN: (纸本)9781665497473

Due to the short decohorence time of qubits available in the NISQ-era, it is essential to pack (minimize the size and or the depth of) a logical quantum circuit as efficiently as possible given a sparsely coupled physical architecture. In this work we introduce a localityaware qubit routing algorithm based on a graph theoretic framework. Our algorithm is designed for the grid and certain "grid-like" architectures. We experimentally show the competitiveness of algorithm by comparing it against the approximate token swapping algorithm, which is used as a primitive in many state-of-the-art quantum transpilers. Our algorithm produces circuits of comparable depth (better on random permutations) while being an order of magnitude faster than a typical implementation of the approximate token swapping algorithm.

关键词： qubit routing parallel token swapping grid graphs

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Kernel Launcher: C++ Library for Optimal-Performance Portable CUDA applications

Kernel Launcher: C++ Library for Optimal-Performance Portabl...

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2023 ieee international parallel and distributed processing symposium Workshops, IPDPSW 2023

作者： Heldens, Stijn Van Werkhoven, Ben Netherlands EScience Center Amsterdam Netherlands

ISBN: (纸本)9798350311990

Graphic processing Units (GPUs) have become ubiquitous in scientific computing. However, writing efficient GPU kernels can be challenging due to the need for careful code tuning. To automatically explore the kernel optimization space, several auto-tuning tools - like Kernel Tuner - have been proposed. Unfortunately, these existing auto-tuning tools do not concern themselves with integration of tuning results back into applications, which puts a significant implementation and maintenance burden on application developers. In this work, we present Kernel Launcher: an easy-to-use C++ library that simplifies the creation of highly-tuned CUDA applications. With Kernel Launcher, programmers can capture kernel launches, tune the captured kernels for different setups, and integrate the tuning results back into applications using runtime compilation. To showcase the applicability of Kernel Launcher, we consider a real-world computational fluid dynamics code and tune its kernels for different GPUs, input domains, and precisions. © 2023 ieee.

关键词： Graphics processing unit

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Cuckoo Node Hashing on GPUs 21

Cuckoo Node Hashing on GPUs

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21st ieee international symposium on parallel and distributed Computing (ISPDC)

作者： Javed, Muhammad Zhou, Hao Troendle, David Jang, Byunghyun Univ Mississippi Dept Comp & Informat Sci University MS 38677 USA Penn State Univ Dept Comp Sci & Engn State Coll PA USA

ISBN: (数字)9781665488020

ISBN: (纸本)9781665488020

The hash table finds numerous applications in many different domains, but its potential for non-coalesced memory accesses and execution divergence characteristics impose optimization challenges on GPUs. We propose a novel hash table design, referred to as Cuckoo Node Hashing, which aims to better exploit the massive data parallelism offered by GPUs. At the core of its design, we leverage Cuckoo Hashing, one of known hash table design schemes, in a closed-address manner, which, to our knowledge, is the first attempt on GPUs. We also propose an architecture-aware warp-cooperative reordering algorithm that improves the memory performance and thread divergence of Cuckoo Node Hashing and efficiently increases the likelihood of coalesced memory accesses in hash table operations. Our experiments show that Cuckoo Node Hashing outperforms and scales better than existing state-of-the-art GPU hash table designs such as DACHash and Slab Hash with a peak performance of 5.03 billion queries/second in static searching and 434 billion insertions/second in static building.

关键词： Instruction sets Memory management Buildings Graphics processing units parallel processing Slabs Probes

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