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检索条件"任意字段=20th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPoPP 2015"

共 39 条记录，以下是1-10 订阅

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PERFLOW: A Domain Specific Framework for Automatic Performance Analysis of parallel Applications 22

PERFLOW: A Domain Specific Framework for Automatic Performan...

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27th acm sigplan symposium on principles and practice of parallel programming (ppopp)

作者： Jin, Yuyang Wang, Haojie Zhong, Runxin Zhang, Chen Zhai, Jidong Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9781450392044

Performance analysis is widely used to identify performance issues of parallel applications. However, complex communications and data dependence, as well as the interactions between different kinds of performance issues make high-efficiency performance analysis even harder. Although a large number of performance tools have been designed, accurately pinpointing root causes for such complex performance issues still needs specific in-depth analysis. To implement each such analysis, significant human efforts and domain knowledge are normally required. To reduce the burden of implementing accurate performance analysis, we propose a domain specific programming framework, named PERFLOW. PERFLOW abstracts the step-by-step process of performance analysis as a dataflow graph. this dataflow graph consists of main performance analysis sub-tasks, called passes, which can either be provided by PERFLOW'S built-in analysis library, or be implemented by developers to meet their requirements. Moreover, to achieve effective analysis, we propose a Program Abstraction Graph to represent the performance of a program execution and then leverage various graph algorithms to automate the analysis. We demonstrate the efficacy of PERFLOW by three case studies of real-world applications with up to 700K lines of code. Results show that PERFLOW significantly eases the implementation of customized analysis tasks. In addition, PERFLOW is able to perform analysis and locate performance bugs automatically and effectively.

关键词： Performance Analysis Domain Specific Framework Dataflow Graph

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Scaling Graph Traversal to 281 Trillion Edges with 40 Million Cores 22

Scaling Graph Traversal to 281 Trillion Edges with 40 Millio...

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27th acm sigplan symposium on principles and practice of parallel programming (ppopp)

作者： Cao, Huanqi Wang, Yuanwei Wang, Haojie Lin, Heng Ma, Zixuan Yin, Wanwang Chen, Wenguang Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China Tsinghua Univ BNRist Beijing Peoples R China Peking Univ Sch Comp Sci Beijing Peoples R China Natl Supercomp Ctr Wuxi Wuxi Jiangsu Peoples R China

ISBN: (纸本)9781450392044

Graph processing, especially high-performance graph traversal, plays a more and more important role in data analytics. the successor of Sunway TaihuLight, NEW SUNWAY, is equipped with nearly 10 PB memory and over 40 million cores, which brings the opportunity to process hundreds of trillions of edges graphs. However, the graph with an unprecedented scale also brings severe performance challenges, including load imbalance, poor locality, and irregular access of graph traversal workload. To address the scalability problem, we propose a novel 3-level degree-aware 1.5D graph partitioning, which benefits from both delegated 1D and 2D partitioning. By delegating extremely heavy vertices globally and other heavy vertices on columns and rows in the processes mesh, we break the scalability wall of previous partitioning methods. Together with sub-iteration direction optimization, core group -aware core subgraph segmenting, and a new on-chip sorting mechanism using RMA, we achieve 180,792 GTEPS on a graph with 281 trillion edges, using 103,912 processors with over 40 million cores, achieving 1.75x performance and 8x capacity compared to the previous state of the art and conforming to the Graph 500 BFS benchmark[14].

关键词： massively parallel algorithm breadth-first search heterogeneous architecture

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VAPRO: Performance Variance Detection and Diagnosis for Production-Run parallel Applications 22

VAPRO: Performance Variance Detection and Diagnosis for Prod...

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27th acm sigplan symposium on principles and practice of parallel programming (ppopp)

作者： Zheng, Liyan Zhai, Jidong Tang, Xiongchao Wang, Haojie Yu, Teng Jin, Yuyang Song, Shuaiwen Leon Chen, Wenguang Tsinghua Univ Beijing Peoples R China Sangfor Technol Inc Shenzhen Guangdong Peoples R China Univ Sydney Sydney NSW Australia BNRist Beijing Peoples R China

ISBN: (纸本)9781450392044

Performance variance is a serious problem for parallel applications, which can cause performance degradation and make applications' behavior hard to understand. therefore, detecting and diagnosing performance variance are of crucial importance for users and application developers. However, previous detection approaches either bring too large overhead and hurt applications' performance, or rely on nontrivial source code analysis that is impractical for production-run parallel applications. In this work, we propose VAPRO, a performance variance detection and diagnosis framework for production-run parallel applications. Our approach is based on an important observation that most parallel applications contain code snippets that are repeatedly executed with fixed workload, which can be used for performance variance detection. To effectively identify these snippets at runtime even without program source code, we introduce State Transition Graph (STG) to track program execution and then conduct lightweight workload analysis on STG to locate variance. To diagnose the detected variance, VAPRO leverages a progressive diagnosis method based on a hybrid model leveraging variance breakdown and statistical analysis. Results show that the performance overhead of VAPRO is only 1.38% on average. VAPRO can detect the variance in real applications caused by hardware bugs, memory, and IQ After fixing the detected variance, the standard deviation of the execution time is reduced by up to 73.5%. Compared with the state-of-the-art variance detection tool based on source code analysis, VAPRO achieves 30.0% higher detection coverage.

关键词： Performance Variance Anomaly Detection System Noise

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High-Performance GPU-to-CPU Transpilation and Optimization via High-Level parallel Constructs 23

High-Performance GPU-to-CPU Transpilation and Optimization v...

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28th acm sigplan Annual symposium on principles and practice of parallel programming, ppopp 2023

作者： Moses, William S. Ivanov, Ivan R. Domke, Jens Endo, Toshio Doerfert, Johannes Zinenko, Oleksandr MIT CSAIL United States Tokyo Tech Japan RIKEN Japan LLNL United States Google France

ISBN: (纸本)9798400700156

While parallelism remains the main source of performance, architectural implementations and programming models change with each new hardware generation, often leading to costly application re-engineering. Most tools for performance portability require manual and costly application porting to yet another programming model. We propose an alternative approach that automatically translates programs written in one programming model (CUDA), into another (CPU threads) based on Polygeist/MLIR. Our approach includes a representation of parallel constructs that allows conventional compiler transformations to apply transparently and without modification and enables parallelism-specific optimizations. We evaluate our framework by transpiling and optimizing the CUDA Rodinia benchmark suite for a multi-core CPU and achieve a 58% geomean speedup over handwritten OpenMP code. Further, we show how CUDA kernels from PyTorch can efficiently run and scale on the CPU-only Supercomputer Fugaku without user intervention. Our PyTorch compatibility layer making use of transpiled CUDA PyTorch kernels outperforms the PyTorch CPU native backend by 2.7×. © 2023 Owner/Author.

关键词： Supercomputers

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Advanced synchronization techniques for task-based runtime systems 21

Advanced synchronization techniques for task-based runtime s...

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26th acm sigplan symposium on principles and practice of parallel programming, ppopp 2021

作者： Álvarez, David Sala, Kevin Maroñas, Marcos Roca, Aleix Beltran, Vincenç Barcelona Supercomputing Center Barcelona Spain

ISBN: (纸本)9781450382946

Task-based programming models like OmpSs-2 and OpenMP provide a flexible data-flow execution model to exploit dynamic, irregular and nested parallelism. Providing an efficient implementation that scales well with small granularity tasks remains a challenge, and bottlenecks can manifest in several runtime components. In this paper, we analyze the limiting factors in the scalability of a task-based runtime system and propose individual solutions for each of the challenges, including a wait-free dependency system and a novel scalable scheduler design based on delegation. We evaluate how the optimizations impact the overall performance of the runtime, both individually and in combination. We also compare the resulting runtime against state of the art OpenMP implementations, showing equivalent or better performance, especially for fine-grained tasks. © 2021 acm.

关键词： parallel programming

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parallel binary code analysis 21

Parallel binary code analysis

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26th acm sigplan symposium on principles and practice of parallel programming, ppopp 2021

作者： Meng, Xiaozhu Anderson, Jonathon M. Mellor-Crummey, John Krentel, Mark W. Miller, Barton P. Milaković, Sran Department of Computer Science Rice University Houston TX United States Computer Sciences Department University of Wisconsin-Madison MadisonWI United States

ISBN: (纸本)9781450382946

Binary code analysis is widely used to help assess a program's correctness, performance, and provenance. Binary analysis applications often construct control flow graphs, analyze data flow, and use debugging information to understand how machine code relates to source lines, inlined functions, and data types. To date, binary analysis has been single-threaded, which is too slow for convenient use in performance tuning workflows where it is used to help attribute performance to complex applications with large binaries. this paper describes our design and implementation for accelerating the task of constructing control flow graphs (CFGs) from binaries by using multithreading. Prior research focuses on algorithms for analysis of challenging code constructs encountered while constructing CFGs, including functions sharing code, jump tables, non-returning functions, and tail calls. these algorithms are described from a program analysis perspective and are not suitable for direct parallel implementation. We abstract the task of constructing CFGs as repeated applications of several core CFG operations that include creating functions, basic blocks, and edges. We then derive CFG operation dependency, commutativity, and monotonicity. these operation properties guide our design of a new parallel analysis for constructing CFGs. Using 64 threads, we achieved as much as 25× speedup for constructing CFGs and 8× for a performance analysis tool that leverages our new analysis to recover program structure. © 2021 acm.

关键词： Graphic methods

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parallel and Distributed Bounded Model Checking of Multi-threaded Programs 20

Parallel and Distributed Bounded Model Checking of Multi-thr...

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25th acm sigplan symposium on principles and practice of parallel programming (ppopp)

作者： Inverso, Omar Trubiani, Catia Gran Sasso Sci Inst Laquila Italy

ISBN: (纸本)9781450368186

We introduce a structure-aware parallel technique for context-bounded analysis of concurrent programs. the key intuition consists in decomposing the set of concurrent traces into symbolic subsets that are separately explored by multiple instances of the same decision procedure running in parallel. the decision procedures work on different partitions of the search space without cooperating, whence distribution follows effortlessly. Our experiments on a selection of complex multi-threaded programs show significant analysis speedups and scalability, and greater performance gains than with general-purpose parallel solvers.

关键词： Concurrency Multithreading Sequentialization Software Verification parallel Analysis Bounded Model Checking SAT

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PLUM: Static parallel Program Locality Analysis under Uniform Multiplexing 20

PLUM: Static Parallel Program Locality Analysis under Unifor...

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25th acm sigplan symposium on principles and practice of parallel programming (ppopp)

作者： Liu, Fangzhou Chen, Dong Smith, Wesley Ding, Chen Univ Rochester Rochester NY 14627 USA Natl Univ Def Technol Changsha Peoples R China

ISBN: (纸本)9781450368186

Data movement has a significant impact on program performance. For multithread programs, this impact is amplified, since different threads often interfere with each other by competing for shared cache space. However, recent de facto locality metrics consider either sequential execution only, or derive locality for multithread programs in an inefficient way, i.e. exhaustive simulation. this paper presents PLUM, a compiler solution for timescale locality analysis for parallel programs. Experiments demonstrate that the prediction accuracy is 93.97% on average. PLUM is the first tool that analyzes data locality for parallel programs during compile time;in addition, it provides an approach for efficiently studying the representative interleaving pattern for parallel executions.

关键词： Static analysis Locality Multithread

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parallel Determinacy Race Detection for Futures 20

Parallel Determinacy Race Detection for Futures

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25th acm sigplan symposium on principles and practice of parallel programming (ppopp)

作者： Xu, Yifan Singer, Kyle Lee, I-Ting Angelina Washington Univ St Louis St Louis MO 63130 USA

ISBN: (纸本)9781450368186

the use of futures can generate arbitrary dependences in the computation, making it difficult to detect races efficiently. Algorithms proposed by priorwork to detect races on programs with futures all have to execute the program sequentially. We propose F-Order, the first known parallel race detection algorithm that detects races on programs that use futures. Given a computation with work T-1 and span T-infinity, our algorithm detects races in time O((T-1 lg (k) over cap + k(2))/ P + T-infinity(k + lg r lg (k) over cap)) on P processors, where k is the number of future operations, r is the maximum number of readers per memory location, and (k) over cap is the maximum number of future operations done by a single future task, which is typically small. We have also implemented a prototype system based on the proposed algorithm and empirically demonstrates its practical efficiency and scalability.

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A parallel Sparse Tensor Benchmark Suite on CPUs and GPUs 20

A Parallel Sparse Tensor Benchmark Suite on CPUs and GPUs

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25th acm sigplan symposium on principles and practice of parallel programming (ppopp)

作者： Li, Jiajia Lakshminarasimhan, Mahesh Wu, Xiaolong Li, Ang Olschanowsky, Catherine Barker, Kevin Pacific Northwest Natl Lab Richland WA 99352 USA Univ Utah Salt Lake City UT USA Purdue Univ W Lafayette IN 47907 USA Boise State Univ Boise ID 83725 USA

ISBN: (纸本)9781450368186

Tensor computations present significant performance challenges that impact a wide spectrum of applications. Efforts on improving the performance of tensor computations include exploring data layout, execution scheduling, and parallelism in common tensor kernels. this work presents a benchmark suite for arbitrary-order sparse tensor kernels using state-of-the-art tensor formats: coordinate (COO) and hierarchical coordinate (HiCOO). It demonstrates a set of reference tensor kernel implementations and some observations on Intel CPUs and NVIDIA GPUs. the full paper can be referred to at http://***/abs/2001.00660.

关键词： sparse tensors benchmark GPU roofline model

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