检索结果-内蒙古大学图书馆

International Symposium on parallel and Distributed Processing (IPDPS)

作者： Thomas B. Rolinger Alan Sussman NVIDIA Austin TX USA University of Maryland College Park MD USA

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

Applications that require distributed-memory systems and exhibit irregular memory access patterns present both productivity and performance challenges. This is largely due to the fine-grain communication that arises from irregular memory accesses to distributed data. The Partitioned Global Address Space (PGAS) model provides a globally shared address space and one-sided communication, making it well suited for implementing irregular codes. However, while the PGAS model provides productivity advantages, irregular applications have difficulty achieving high performance due to the cost of fine-grain remote accesses. One way to improve the performance of such PGAS programs is to prefetch remote data before it is needed, thereby hiding communication latency. The challenges of applying prefetching are computing the prefetch distance (i.e., how far ahead to issue prefetches), determining when prefetching will be profitable, and modifying the program to perform prefetching. In this work, we present an adaptive prefetching optimization that can be applied to PGAS programs with irregular memory access patterns. We target the Chapel parallel programming language, which implements a PGAS model. Our optimization leverages runtime information to adjust the prefetch distance and to pause/resume prefetching when that is likely to provide performance gains. Furthermore, we develop compiler support to automatically apply the optimization without requiring user intervention. We evaluate the optimization across five different distributed-memory systems and four workloads. We observe runtime speed-ups of 0.5 – 377x for the Chapel workloads when compared to not prefetching, and 3 – 215x when compared to implementations written in UPC, OpenSHMEM, and one-sided MPI.

关键词： Productivity Distributed processing Runtime parallel programming Prefetching Memory management Distributed databases

来源：评论

学校读者我要写书评

暂无评论

Towards Accelerating k-NN with MPI and Near-Memory Processing

Towards Accelerating k-NN with MPI and Near-Memory Processin...

引用

IEEE International Symposium on parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Hooyoung Ahn Seonyoung Kim Yoomi Park Woojong Han Nick Contini Bharath Ramesh Mustafa Abduljabbar Dhabaleswar K. Panda Supercomputing Technology Research Center Electronics and Telecommunications Research Institute Department of Computer Science and Engineering The Ohio State University

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

Message Passing Interface (MPI) is a common parallel programming model in High-Performance Computing fields. Recently, it has been widely used in Artificial Intelligence (AI) applications. However, the performance of those applications is limited by the memory wall problem, which is the performance gap between the processor and memory. To address these problems, we propose a novel computing architecture for accelerating k-NN query, which is the key operation of AI applications. The proposed computing architecture involves two or more computing nodes within a rack using the memory of an NMP device as a communication buffer and the accelerator of the NMP device for MPI collective communication. The superiority of the proposed method is that it reduces data copying during local computation and eliminates the network cost of gathering local computation results for global computation. Furthermore, it can enhance performance by processing data in the device's memory through near-memory processing. However, since these NMP devices are still in development, with no commercially available products currently, we have undertaken the task of developing them ourselves. Despite these circumstances, to demonstrate the feasibility of our proposed approach, we have implemented it in a single-node environment without making any unreasonable assumptions and have compared it with conventional approaches. Although we are using a single-node environment, we have designed a more sophisticated architecture, analyzed the time complexity, and compared the estimated performance of both the conventional and proposed approaches. We can conclude that the proposed computing architecture is feasible, and we anticipate an even more performance enhancement in a multi-node environment.

关键词： Performance evaluation Costs parallel programming Message passing High performance computing Conferences Computer architecture

来源：评论

学校读者我要写书评

暂无评论

UniFaaS: programming across Distributed Cyberinfrastructure with Federated Function Serving

UniFaaS: Programming across Distributed Cyberinfrastructure ...

引用

International Symposium on parallel and Distributed Processing (IPDPS)

作者： Yifei Li Ryan Chard Yadu Babuji Kyle Chard Ian Foster Zhuozhao Li Dept. of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Data Science and Learning Division Argonne National Laboratory Lemont IL USA Dept. of Computer Science University of Chicago Chicago IL USA

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

Modern scientific applications are increasingly decomposable into individual functions that may be deployed across distributed and diverse cyberinfrastructure such as supercomputers, clouds, and accelerators. Such applications call for new approaches to programming, distributed execution, and function-level management. We present UniFaaS, a parallel programming framework that relies on a federated function-as-a-service (FaaS) model to enable composition of distributed, scalable, and high-performance scientific workflows, and to support fine-grained function-level management. UniFaaS provides a unified programming interface to compose dynamic task graphs with transparent wide-area data management. UniFaaS exploits an observe-predict-decide approach to efficiently map workflow tasks to target heterogeneous and dynamic resources. We propose a dynamic heterogeneity-aware scheduling algorithm that employs a delay mechanism and a re-scheduling mechanism to accommodate dynamic resource capacity. Our experiments show that UniFaaS can efficiently execute workflows across computing resources with minimal scheduling overhead. We show that UniFaaS can improve the performance of a real-world drug screening workflow by as much as 22.99% when employing an additional 19.48% of resources and a montage workflow by 54.41% when employing an additional 47.83% of resources across multiple distributed clusters, in contrast to using a single cluster.

关键词： Scheduling algorithms parallel programming Heuristic algorithms parallel processing Elasticity Dynamic scheduling Supercomputers

来源：评论

学校读者我要写书评

暂无评论

Computation Augmentation Techniques for Computing Continuum

Computation Augmentation Techniques for Computing Continuum

引用

Cluster, Cloud and Internet Computing Workshops (CCGridW), IEEE/ACM International Symposium on

作者： George-Mircea Grosu Silvia-Elena Nistor Radu-Ioan Ciobanu Joanna Kolodziej Florin Pop National University of Science and Technology Politehnica Bucharest Romania NASK Warsaw and Cracow University of Technology Poland Department of Computer Sciences Cracow University of Technology Cracow Poland National Institute for Research and Development in Informatics (ICI) Bucharest Romania Academy of Romanian Scientists Bucharest Romania

ISBN: (数字)9798350377514

ISBN: (纸本)9798350377521

We are witnessing a tremendous expansion in computational power, leading to monumental technological advances with various practical applications. The need for parallelism originated from the limitations of single-processing performance. Increasing the transistor density on integrated circuits was the way to go, thus creating faster and more complex monolithic processors. However, these circuits approached their limits of hardware expansion, so the single-processor model has become unreliable and insufficient overall. This is how multicore architectures were born and, with them, the notion of parallel computing. Our research background in the Drop Computing paradigm and Interest in studying parallel programming paradigms encouraged us to propose a new approach towards parallel processing in mobile, ad-hoc, opportunistic networks. This paper adapts one fundamental message-passing standard for parallel architectures within the Drop Computing context. We define a new library DroMPI. Besides the challenges of parallel programming, the solution has to address the challenges imposed by hardware constraints, limited resources, and the decentralized model specific to Drop Computing.

关键词： Program processors parallel programming Multicore processing Computational modeling parallel processing Hardware Libraries Transistors Integrated circuit modeling Standards

来源：评论

学校读者我要写书评

暂无评论

High-level Stream Processing: A Complementary Analysis of Fault Recovery

arXiv

引用

arXiv 2024年

作者： Vogel, Adriano Henning, Sören Perez-Wohlfeil, Esteban Ertl, Otmar Rabiser, Rick JKU Dynatrace Co-Innovation Lab LIT CPS Lab Johannes Kepler University Linz Linz Austria Dynatrace Research Linz Austria LIT CPS Lab Johannes Kepler University Linz Linz Austria

parallel computing is very important to accelerate the performance of software systems. Additionally, considering that a recurring challenge is to process high data volumes continuously, stream processing emerged as a paradigm and software architectural style. Several software systems rely on stream processing to deliver scalable performance, whereas open-source frameworks provide coding abstraction and high-level parallel computing. Although stream processing's performance is being extensively studied, the measurement of fault tolerance-a key abstraction offered by stream processing frameworks-has still not been adequately measured with comprehensive testbeds. In this work, we extend the previous fault recovery measurements with an exploratory analysis of the configuration space, additional experimental measurements, and analysis of improvement opportunities. We focus on robust deployment setups inspired by requirements for near real-time analytics of a large cloud observability platform. The results indicate significant potential for improving fault recovery and performance. However, these improvements entail grappling with configuration complexities, particularly in identifying and selecting the configurations to be fine-tuned and determining the appropriate values for them. Therefore, new abstractions for transparent configuration tuning are also needed for large-scale industry setups. We believe that more software engineering efforts are needed to provide insights into potential abstractions and how to achieve them. The stream processing community and industry practitioners could also benefit from more interactions with the high-level parallel programming community, whose expertise and insights on making parallel programming more productive and efficient could be extended. Copyright © 2024, The Authors. All rights reserved.

关键词： parallel programming

来源：评论

学校读者我要写书评

暂无评论

Taskgrind: Heavyweight Dynamic Binary Instrumentation for parallel Programs Analysis

Taskgrind: Heavyweight Dynamic Binary Instrumentation for Pa...

引用

High Performance Computing, Networking, Storage and Analysis, SC-W: Workshops of the International Conference for

作者： Romain Pereira George Stelle Patrick Carribault Avalon LIP ENS Inria Lyon France Los Alamos National Laboratory Los Alamos New Mexico United States CEA DAM DIFF-91297 Arpajon Université Paris-Saclay CEA Laboratoire en Informatique Haute Performance pour le Calcul et la Simulation Bruyères-le-Châtel France

ISBN: (数字)9798350355543

ISBN: (纸本)9798350355550

Determinacy races are concurrent programming hazards occurring when two accesses on the same memory address are not ordered, and at least one is writing. Their presence hints at a correctness error, particularly under asynchronous task-based parallel programming models. This paper introduces Taskgrind: a Valgrind tool for memory access analysis of parallel programming models such as Cilk or OpenMP. We illustrate the tool’s capabilities with a determinacy-race analysis and confront it with state-of-the-art tools. Results show fewer false negatives and memory overheads on a set of microbenchmarks and LULESH, with meaningful error reports toward assisting programmers when parallelizing programs.

关键词： Concurrent computing Analytical models parallel programming Instruments High performance computing Conferences Writing Hazards Performance analysis

来源：评论

学校读者我要写书评

暂无评论

Should AI Optimize Your Code? A Comparative Study of Current Large Language Models Versus Classical Optimizing Compilers

arXiv

引用

arXiv 2024年

作者： Rosas, Miguel Romero Sanchez, Miguel Torres Eigenmann, Rudolf University of Delaware NewarkDE United States

In the contemporary landscape of computer architecture, the demand for efficient parallel programming persists, needing robust optimization techniques. Traditional optimizing compilers have historically been pivotal in this endeavor, adapting to the evolving complexities of modern software systems. The emergence of Large Language Models (LLMs) raises intriguing questions about the potential for AI-driven approaches to revolutionize code optimization methodologies. This paper presents a comparative analysis between two state-of-the-art Large Language Models, GPT-4.0 and CodeLlama-70B, and traditional optimizing compilers, assessing their respective abilities and limitations in optimizing code for maximum efficiency. Additionally, we introduce a benchmark suite of challenging optimization patterns and an automatic mechanism for evaluating performance and correctness of the code generated by such tools. We used two different prompting methodologies to assess the performance of the LLMs - Chain of Thought (CoT) and Instruction Prompting (IP). We then compared these results with three traditional optimizing compilers, CETUS, PLUTO and ROSE, across a range of real-world use cases. A key finding is that while LLMs have the potential to outperform current optimizing compilers, they often generate incorrect code on large code sizes, calling for automated verification methods. Our extensive evaluation across 3 different benchmarks suites shows CodeLlama-70B as the superior optimizer among the two LLMs, capable of achieving speedups of up to 2.1x. Additionally, CETUS is the best among the optimizing compilers, achieving a maximum speedup of 1.9x. We also found no significant difference between the two prompting methods: Chain of Thought (Cot) and Instructing prompting (IP). © 2024, CC BY.

关键词： parallel programming

来源：评论

学校读者我要写书评

暂无评论

GPA: A GPU Performance Advisor Based on Instruction Sampling 21

GPA: A GPU Performance Advisor Based on Instruction Sampling

引用

19th IEEE/ACM International Symposium on Code Generation and Optimization (CGO)

作者： Zhou, Keren Meng, Xiaozhu Sai, Ryuichi Mellor-Crummey, John Rice Univ Dept Comp Sci Houston TX 77005 USA

ISBN: (纸本)9781728186139

Developing efficient GPU kernels can be difficult because of the complexity of GPU architectures and programming models. Existing performance tools only provide coarse-grained tuning advice at the kernel level, if any. In this paper, we describe GPA, a performance advisor for NVIDIA GPUs that suggests potential code optimizations at a hierarchy of levels, including individual lines, loops, and functions. To relieve users of the burden of interpreting performance counters and analyzing bottlenecks, GPA uses data flow analysis to approximately attribute measured instruction stalls to their root causes and uses information about a program's structure and the GPU to match inefficiency patterns with optimization strategies. To quantify the potential benefits of each optimization strategy, we developed PC sampling-based performance models to estimate its speedup. Our experiments with benchmarks and applications show that GPA provides insightful reports to guide performance optimization. Using GPA, we obtained speedups on a Volta V100 GPU ranging from 1.01x to 3.58x, with a geometric mean of 1.22x.

关键词： High performance computing Performance analysis parallel programming parallel architectures

来源：评论

学校读者我要写书评

暂无评论

Frame Rate Latency Reduction for Real-time Vehicle Tracking using Network Cameras

Frame Rate Latency Reduction for Real-time Vehicle Tracking ...

引用

IEEE Region 10 Symposium (TENSYMP) - Good Technologies for Creating Future

作者： Cempron, Jonathan Paul C. Bautista, Carlo Migel Cu, Gregory Ilao, Joel P. De La Salle Univ Comp Technol Dept Coll Comp Studies Manila Philippines De La Salle Univ Software Technol Dept Coll Comp Studies Manila Philippines

ISBN: (纸本)9781665400268

Traffic monitoring and vehicle counting systems that use surveillance cameras employ several computer vision techniques, one of which is object tracking, which approximates the trajectory of the vehicle throughout the scene. However, a major challenge in processing videos from network camera feeds is the irregular and low frame rates, affecting the performance of object tracking. In this paper, we present a concurrent implementation framework intended to increase the input network video frame rate.

关键词： real-time network camera parallel programming computer vision object tracking

来源：评论

学校读者我要写书评

暂无评论

Lightweight Function Monitors for Fine-Grained Management in Large Scale Python Applications 35

Lightweight Function Monitors for Fine-Grained Management in...

引用

35th IEEE International parallel and Distributed Processing Symposium (IPDPS)

作者： Shaffer, Tim Li, Zhuozhao Tovar, Ben Babuji, Yadu Dasso, T. J. Surma, Zoe Chard, Kyle Foster, Ian Thain, Douglas Univ Notre Dame Notre Dame IN 46556 USA Univ Chicago Chicago IL 60637 USA Argonne Natl Lab Argonne IL 60439 USA

ISBN: (纸本)9781665440660

Python has become a widely used programming language for research, not only for small one-off analyses, but also for complex application pipelines running at supercomputer-scale. Modern parallel programming frameworks for Python present users with a more granular unit of management than traditional Unix processes and batch submissions: the Python function. We review the challenges involved in running native Python functions at scale, and present techniques for dynamically determining a minimal set of dependencies and for assembling a lightweight function monitor (LFM) that captures the software environment and manages resources at the granularity of single functions. We evaluate these techniques in a range of environments, from campus cluster to supercomputer, and show that our advanced dependency management planning and dynamic resource management methods provide superior performance and utilization relative to coarser-grained management approaches, achieving several-fold decrease in execution time for several large Python applications.

关键词： parallel programming Pipelines Tools Software Supercomputers Planning Resource management

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：