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检索条件"任意字段=ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming"

共 757 条记录，以下是81-90 订阅

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CuPBoP: A Framework to Make CUDA Portable 23

CuPBoP: A Framework to Make CUDA Portable

引用

Proceedings of the 28th acm sigplan Annual symposium on principles and practice of parallel programming

作者： Ruobing Han Jun Chen Bhanu Garg Jeffrey Young Jaewoong Sim Hyesoon Kim Georgia Institute of Technology Seoul National University

ISBN: (纸本)9798400700156

CUDA, as one of the most popular choices for GPU programming, can be executed only on NVIDIA GPUs. To execute CUDA on non-NVIDIA devices, researchers have proposed to translate CUDA to other programming languages. However, this approach cannot achieve high coverage due to the challenges in source-to-source *** propose a framework, CuPBoP, that executes CUDA programs on non-NVIDIA devices without relying on other programming languages. CuPBoP consists of two parts. The compilation part applies transformations on CUDA host/kernel IRs. The runtime part consists of the runtime libraries for CUDA built-in functions. For the CPU backends, compared with the existing frameworks, CuPBoP achieves the highest coverage on all CPUs that we evaluate (x86, aarch64, RISC-V).We make CuPBoP publicly available to inspire more works in this area 1.

关键词： AArch64 X86 portable programming CUDA RISC-V

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Automatic differentiation of parallel loops with formal methods 22

Automatic differentiation of parallel loops with formal meth...

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Proceedings of the 27th acm sigplan symposium on principles and practice of parallel programming

作者： Jan Hückelheim Laurent Hascoët Argonne National Laboratory Inria Sophia Antipolis

ISBN: (纸本)9781450392044

The accompanying poster to this short paper presents a combination of reverse mode AD and formal methods to enable efficient differentiation of (or backpropagation through) shared-memory parallel code. Compared to the state of the art, our approach can more often avoid the need for atomic updates or private data copies during the parallel derivative computation, even in the presence of unstructured or data-dependent data access patterns. This is achieved by gathering information about the memory access patterns from the input program, which is assumed to be correctly parallelized. This information is then used to build a model of assertions in a theorem prover, which can be used to check the safety of shared memory accesses during the parallel derivative computation.

关键词： OpenMP

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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 algorithms for masked sparse matrix-matrix products 22

Parallel algorithms for masked sparse matrix-matrix products

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Proceedings of the 27th acm sigplan symposium on principles and practice of parallel programming

作者： Srđan Milaković Oguz Selvitopi Israt Nisa Zoran Budimlić Aydin Buluç Rice University Lawrence Berkeley Nat. Laboratory AWS AI

ISBN: (纸本)9781450392044

Computing the product of two sparse matrices (SpGEMM) is a fundamental operation in various combinatorial and graph algorithms as well as various bioinformatics and data analytics applications for computing inner-product similarities. For an important class of algorithms, only a subset of the output entries are needed, and the resulting operation is known as Masked SpGEMM since a subset of the output entries is considered to be "masked out". In this work, we investigate various novel algorithms and data structures for this rather challenging and important computation, and provide guidelines on how to design a fast Masked-SpGEMM for shared-memory architectures.

关键词： GraphBLAS

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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 Design of GPU-Based Quantitative Model Checking 1

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22nd International Conference on Verification, Model Checking, and Abstract Interpretation (VMCAI) jointly with the 48th acm sigplan symposium on principles of programming Languages (POPL)

作者： Kwon, YoungMin Kim, Eunhee State Univ New York Dept Comp Sci Incheon South Korea 2e Consulting Corp Seoul South Korea

ISBN: (数字)9783030670672

ISBN: (纸本)9783030670665;9783030670672

In this paper, we implement a GPU-based quantitative model checker and compare its performance with a CPU-based one. Linear Temporal Logic for Control (LTLC) is a quantitative variation of LTL to describe properties of a linear system and LTLC-Checker [1] is an implementation of its model checking algorithm. In practice, its long and unpredictable execution time has been a concern in applying the technique to real-time applications such as automatic control systems. In this paper, we design an LTLC model checker using a GPGPU programming technique. The resulting model checker is not only faster than the CPU-based one especially when the problem is not simple, but it has less variation in the execution time as well. Furthermore, multiple counterexamples can be found together when the CPU-based checker can find only one.

关键词： Quantitative model checking GPU programming LTLC

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LB-HM: load balance-aware data placement on heterogeneous memory for task-parallel HPC applications 22

LB-HM: load balance-aware data placement on heterogeneous me...

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Proceedings of the 27th acm sigplan symposium on principles and practice of parallel programming

作者： Zhen Xie Jie Liu Sam Ma Jiajia Li Dong Li University of California College of William & Mary

ISBN: (纸本)9781450392044

The emergence of heterogeneous memory (HM) provides a cost-effective and high-performance solution to memory-consuming HPC applications. However, using HM, wisely migrating data objects on it is critical for high performance. In this work, we introduce a load balance-aware page management system, named LB-HM. LB-HM introduces task semantics during memory profiling, rather than being application-agnostic. Evaluating with a set of memory-consuming HPC applications, we show that we show that LB-HM reduces existing load imbalance and leads to an average of 17.1% and 15.4% (up to 26.0% and 23.2%) performance improvement, compared with a hardware-based solution and an industry-quality software-based solution on Optane-based HM.

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A parallel branch-and-bound algorithm with history-based domination 22

A parallel branch-and-bound algorithm with history-based dom...

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Proceedings of the 27th acm sigplan symposium on principles and practice of parallel programming

作者： Taspon Gonggiatgul Ghassan Shobaki Pinar Muyan-Özçelik California State University

ISBN: (纸本)9781450392044

In this paper, we describe a parallel Branch-and-Bound (B&B) algorithm with a history-based domination technique, and we apply it to the Sequential Ordering Problem (SOP). To the best of our knowledge, the proposed algorithm is the first parallel B&B algorithm that includes a history-based domination technique and is the first parallel B&B algorithm for solving the SOP using a pure B&B approach. The proposed algorithm takes a pool-based approach and employs a collection of novel techniques that we have developed to achieve effective parallel exploration of the solution space, including parallel history domination, history table memory management, and a thread restart technique. The proposed algorithm was experimentally evaluated using the SOPLIB and TSPLIB benchmarks. The results show that using ten threads with a time limit of one hour on the medium-difficulty instances, the proposed algorithm gives a geometric-mean speedup of 19.9 on SOPLIB and 10.23 on TSPLIB, with super-linear speedups up to 65x seen on 17 instances.

关键词： NP-complete problems

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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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XIndex: A Scalable Learned Index for Multicore Data Storage 20

XIndex: A Scalable Learned Index for Multicore Data Storage

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

作者： Tang, Chuzhe Wang, Youyun Dong, Zhiyuan Hu, Gansen Wang, Zhaoguo Wang, Minjie Chen, Haibo Shanghai Jiao Tong Univ Inst Parallel & Distributed Syst Shanghai Peoples R China Shanghai Jiao Tong Univ Shanghai Key Lab Scalable Comp & Syst Shanghai Peoples R China NYU Dept Comp Sci New York NY 10003 USA

ISBN: (纸本)9781450368186

We present XIndex, a concurrent ordered index designed for fast queries. Similar to a recent proposal of the learned index, XIndex uses learned models to optimize index efficiency. Comparing with the learned index, XIndex is able to effectively handle concurrent writes without affecting the query performance by leveraging fine-grained synchronization and a new compaction scheme, Two-Phase Compaction. Furthermore, XIndex adapts its structure according to runtime workload characteristics to support dynamic workload. We demonstrate the advantages of XIndex with both YCSB and TPC-C (KV), a TPC-C variant for key-value stores. XIndex achieves up to 3.2x and 4.4x performance improvement comparing with Masstree and Wormhole, respectively, on a 24-core machine, and it is open-sourced(1).

关键词： Compaction

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