Proceedings of the 2023 ACM Workshop on Highlights of Parallel Computing最新文献

Provably Fast and Space-Efficient Parallel Biconnectivity (Abstract) 可证明快速且空间高效的并行双连接(摘要)

Proceedings of the 2023 ACM Workshop on Highlights of Parallel Computing Pub Date : 2023-07-18 DOI: 10.1145/3597635.3598018

Xiaojun Dong, Letong Wang, Yan Gu, Yihan Sun

引用次数: 1

Empirical Challenge for NC Theory (Abstract) NC理论的经验挑战(摘要)

Proceedings of the 2023 ACM Workshop on Highlights of Parallel Computing Pub Date : 2023-07-18 DOI: 10.1145/3597635.3598020

Ananth Hari, U. Vishkin

引用次数: 0

Smarter Atomic Smart Pointers: Safe and Efficient Concurrent Memory Management (Abstract) 更智能的原子智能指针:安全高效的并发内存管理(摘要)

Proceedings of the 2023 ACM Workshop on Highlights of Parallel Computing Pub Date : 2023-07-18 DOI: 10.1145/3597635.3598027

Daniel Anderson, G. Blelloch, Yuanhao Wei

引用次数: 0

Efficient Construction of Directed Hopsets and Parallel Single-source Shortest Paths (Abstract) 有向hopset和并行单源最短路径的高效构造(摘要)

Proceedings of the 2023 ACM Workshop on Highlights of Parallel Computing Pub Date : 2023-07-18 DOI: 10.1145/3597635.3598019

Nairen Cao, Jeremy T. Fineman, Katina Russell

{"title":"Efficient Construction of Directed Hopsets and Parallel Single-source Shortest Paths (Abstract)","authors":"Nairen Cao, Jeremy T. Fineman, Katina Russell","doi":"10.1145/3597635.3598019","DOIUrl":"https://doi.org/10.1145/3597635.3598019","url":null,"abstract":"The single-source shortest-path problem is as follows: given a graph with nonnegative edge weights and a designated source vertex s, return the distances from~s to each other vertex such. This paper presents a randomized parallel single-source shortest paths (SSSP) algorithm for directed graphs with non-negative integer edge weights that solves the exact SSSP problem in O (m) work and n^1/2+o(1) span, with high probability. All previous exact SSSP algorithms with nearly linear work have linear span, even for undirected unweighted graphs. To solve exact SSSP problem, we first show a deterministic reduction from exact SSSP to directed hopsets using the iterative gradual rounding technique. An (β, ε)-hopset is a set of weighted edges, also known as shortcuts, that when added to the graph, admit β-hop paths with weights no more than (1 + ε) times the true shortest path distances. We show that (β, ε)-hopsets can be used to solve the exact SSSP problem in O (m) work and O (β) span. Furthermore, we present the first nearly linear-work algorithm for constructing hopsets on directed graphs. Our sequential algorithm runs in O (m) time and constructs a hopset with O (n) edges and β = n^1/2+o(1) . We also provide a parallel version of the algorithm with O (m) work and n^1/2+o(1) span. The directed hopsets can be used to solve approximate SSSP problems efficiently, where the objective is to return estimates of the distances from the source vertex to every other vertex such that the estimate falls between the true distance and (1+ε) times the distance. Specifically, for constant ε and graphs with polynomially-bounded real edge weights, there is an algorithm solving approximate SSSP problem with O (m) work and n^1/2+o(1) span.","PeriodicalId":185981,"journal":{"name":"Proceedings of the 2023 ACM Workshop on Highlights of Parallel Computing","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123777515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Taming Misaligned Graph Traversals in Concurrent Graph Processing (Abstract) 在并发图处理中驯服不对齐的图遍历(摘要)

Proceedings of the 2023 ACM Workshop on Highlights of Parallel Computing Pub Date : 2023-07-18 DOI: 10.1145/3597635.3598028

Xizhe Yin, Zhijia Zhao, Rajiv Gupta

引用次数: 0

Parallel Strong Connectivity Based on Faster Reachability (Abstract) 基于更快可达性的并行强连接(摘要)

Proceedings of the 2023 ACM Workshop on Highlights of Parallel Computing Pub Date : 2023-07-18 DOI: 10.1145/3597635.3598017

Letong Wang, Xiaojun Dong, Yan Gu, Yihan Sun

引用次数: 1

Fast Parallel Algorithms for Euclidean Minimum Spanning Tree and Hierarchical Spatial Clustering (Abstract) 欧几里得最小生成树与分层空间聚类的快速并行算法(摘要)

Proceedings of the 2023 ACM Workshop on Highlights of Parallel Computing Pub Date : 2023-07-18 DOI: 10.1145/3597635.3598025

Yiqiu Wang, Shangdi Yu, Yan Gu, Julian Shun

{"title":"Fast Parallel Algorithms for Euclidean Minimum Spanning Tree and Hierarchical Spatial Clustering (Abstract)","authors":"Yiqiu Wang, Shangdi Yu, Yan Gu, Julian Shun","doi":"10.1145/3597635.3598025","DOIUrl":"https://doi.org/10.1145/3597635.3598025","url":null,"abstract":"This paper presents new parallel algorithms for generating Euclidean minimum spanning trees and spatial clustering hierarchies (known as HDBSCAN^*). Our approach is based on generating a well-separated pair decomposition followed by using Kruskal's minimum spanning tree algorithm and bichromatic closest pair computations. We introduce a new notion of well-separation to reduce the work and space of our algorithm for HDBSCAN^*. We also give a new parallel divide-and-conquer algorithm for computing the dendrogram and reachability plots, which are used in visualizing clusters of different scale that arise for both EMST and HDBSCAN^*. We show that our algorithms are theoretically efficient: they have work (number of operations) matching their sequential counterparts, and polylogarithmic depth (parallel time). We implement our algorithms and propose a memory optimization that requires only a subset of well-separated pairs to be computed and materialized, leading to savings in both space (up to 10x) and time (up to 8x). Our experiments on large real-world and synthetic data sets using a 48-core machine show that our fastest algorithms outperform the best serial algorithms for the problems by 11.13--55.89x, and existing parallel algorithms by at least an order of magnitude.","PeriodicalId":185981,"journal":{"name":"Proceedings of the 2023 ACM Workshop on Highlights of Parallel Computing","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125118289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Static Prediction of Parallel Computation Graphs (Abstract) 并行计算图的静态预测(摘要)

Proceedings of the 2023 ACM Workshop on Highlights of Parallel Computing Pub Date : 2023-07-18 DOI: 10.1145/3597635.3598026

Stefan K. Muller

{"title":"Static Prediction of Parallel Computation Graphs (Abstract)","authors":"Stefan K. Muller","doi":"10.1145/3597635.3598026","DOIUrl":"https://doi.org/10.1145/3597635.3598026","url":null,"abstract":"Many results in the theory of parallel scheduling, dating back to Brent's Theorem, are expressed in terms of the parallel dependency structure of a program as represented by a Directed Acyclic Graph (DAG). In the world of parallel and concurrent program analysis, such DAG models are also used to study deadlock, data races, and priority inversions, to name just a few examples. In all of these cases, it tends to be convenient to think of the DAG as a model of the program itself-we might say, for example, that the time to run a parallel program on P processors depends on the work and span of the program's DAG. This assumes that the DAG is a static, predictable property of the program. In reality, however, a DAG typically models the runtime relationships between threads during a particular execution of a program. To obtain the DAG, one might simulate an execution (or all possible executions) using some form of cost semantics, a dynamic semantics that produces the DAG as it executes the program. In fine-grained parallel programs, such as those that result from constructs such as fork/join, spawn/sync, async/finish, and futures, these DAGs tend to be especially dynamic and dependent on the features of a particular execution. For example, a divide-and-conquer algorithm implemented using fork/join parallelism may divide a certain number of times depending on the input size, and a program written with futures can choose to wait on threads or not wait on threads depending on conditions available only at runtime. Such programs are best represented by a (possibly infinite) family of DAGs, representing all possible executions of the program.","PeriodicalId":185981,"journal":{"name":"Proceedings of the 2023 ACM Workshop on Highlights of Parallel Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130258529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CommonGraph: Graph Analytics on Evolving Data (Abstract) CommonGraph:演化数据的图分析(摘要)

Proceedings of the 2023 ACM Workshop on Highlights of Parallel Computing Pub Date : 2023-07-18 DOI: 10.1145/3597635.3598022

Mahbod Afarin, Chao Gao, Shafiur Rahman, Nael B. Abu-Ghazaleh, Rajiv Gupta

引用次数: 1

Accelerating Sparse Data Orchestration via Dynamic Reflexive Tiling (Extended Abstract) 利用动态自反平铺加速稀疏数据编排(扩展摘要)

Proceedings of the 2023 ACM Workshop on Highlights of Parallel Computing Pub Date : 2023-07-18 DOI: 10.1145/3597635.3598031

Toluwanimi O. Odemuyiwa, Hadi Asghari-Moghaddam, Michael Pellauer, Kartik Hegde, Po-An Tsai, N. Crago, A. Jaleel, J. Owens, Edgar Solomonik, J. Emer, Christopher W. Fletcher

{"title":"Accelerating Sparse Data Orchestration via Dynamic Reflexive Tiling (Extended Abstract)","authors":"Toluwanimi O. Odemuyiwa, Hadi Asghari-Moghaddam, Michael Pellauer, Kartik Hegde, Po-An Tsai, N. Crago, A. Jaleel, J. Owens, Edgar Solomonik, J. Emer, Christopher W. Fletcher","doi":"10.1145/3597635.3598031","DOIUrl":"https://doi.org/10.1145/3597635.3598031","url":null,"abstract":"Tensor algebra involving multiple sparse operands is severely memory bound, making it a challenging target for acceleration. Furthermore, irregular sparsity complicates traditional techniques---such as tiling---for ameliorating memory bottlenecks. Prior sparse tiling schemes are sparsity unaware: they carve tensors into uniform coordinate-space shapes, which leads to low-occupancy tiles and thus lower exploitable reuse. To address these challenges, this paper proposes dynamic reflexive tiling (DRT), a novel tiling method that improves data reuse over prior art for sparse tensor kernels, unlocking significant performance improvement opportunities. DRT's key idea is dynamic sparsity-aware tiling. DRT continuously re-tiles sparse tensors at runtime based on the current sparsity of the active regions of all input tensors, to maximize accelerator buffer utilization while retaining the ability to co-iterate through tiles of distinct tensors. Through an extensive evaluation over a set of SuiteSparse matrices, we show how DRT can be applied to multiple prior accelerators with different dataflows (ExTensor, OuterSPACE, MatRaptor), improving their performance (by 3.3x, 5.1x, and 1.6x, respectively) while adding negligible area overhead. We apply DRT to higher-order tensor kernels to reduce DRAM traffic by 3.9x and 16.9x over a CPU implementation and prior-art tiling scheme, respectively. Finally, we show that the technique is portable to software, with an improvement of 7.29x and 2.94x in memory overhead compared to untiled sparse-sparse matrix multiplication (SpMSpM).","PeriodicalId":185981,"journal":{"name":"Proceedings of the 2023 ACM Workshop on Highlights of Parallel Computing","volume":"95 Suppl A 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116920170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1