{"title":"Session details: Full Papers Session 3","authors":"T. Suzumura","doi":"10.1145/3260997","DOIUrl":"https://doi.org/10.1145/3260997","url":null,"abstract":"","PeriodicalId":20568,"journal":{"name":"Proceedings of the ACM Workshop on High Performance Graph Processing","volume":"47 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76510071","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}
It is our great pleasure to welcome you to the 2016 High Performance Graph Processing Workshop -- HPGP'16. This inaugural workshop of the HPGP workshop series is focused on multiple aspects of graph processing on high performance computing systems. The mission of the workshop is to serve as a platform for dissemination of cutting edge research conducted on high performance graph processing and identify new directions for future research and development. HPGP'16 provides researchers and practitioners a unique opportunity to share their perspectives with others interested in the various aspects of high performance graph processing. The call for papers attracted submissions mainly from Asia and the United States. We also encourage attendees to attend the keynote talk. This valuable and insightful talk can and will guide us to a better understanding of the future of high performance graph processing: Towards next-generation graph processing and management platform, Toyotaro Suzumura (who is currently at IBM T.J. Watson Research Center).
我们非常高兴地欢迎您参加2016年高性能图形处理研讨会——HPGP'16。这是HPGP系列研讨会的首个研讨会，重点关注高性能计算系统上图形处理的多个方面。研讨会的使命是作为一个平台，传播在高性能图形处理方面进行的前沿研究，并为未来的研究和发展确定新的方向。HPGP'16为研究人员和从业者提供了一个独特的机会，与对高性能图形处理的各个方面感兴趣的其他人分享他们的观点。论文征集主要吸引了来自亚洲和美国的投稿。我们也鼓励与会者参加主题演讲。这个有价值和有见地的演讲可以并将引导我们更好地理解高性能图形处理的未来:迈向下一代图形处理和管理平台，丰田太郎Suzumura(他目前在IBM T.J. Watson研究中心)。
{"title":"Proceedings of the ACM Workshop on High Performance Graph Processing","authors":"T. Suzumura, D. García-Gasulla, Miyuru Dayarathna","doi":"10.1145/2915516","DOIUrl":"https://doi.org/10.1145/2915516","url":null,"abstract":"It is our great pleasure to welcome you to the 2016 High Performance Graph Processing Workshop -- HPGP'16. This inaugural workshop of the HPGP workshop series is focused on multiple aspects of graph processing on high performance computing systems. The mission of the workshop is to serve as a platform for dissemination of cutting edge research conducted on high performance graph processing and identify new directions for future research and development. HPGP'16 provides researchers and practitioners a unique opportunity to share their perspectives with others interested in the various aspects of high performance graph processing. \u0000 \u0000The call for papers attracted submissions mainly from Asia and the United States. \u0000 \u0000We also encourage attendees to attend the keynote talk. This valuable and insightful talk can and will guide us to a better understanding of the future of high performance graph processing: \u0000Towards next-generation graph processing and management platform, Toyotaro Suzumura (who is currently at IBM T.J. Watson Research Center).","PeriodicalId":20568,"journal":{"name":"Proceedings of the ACM Workshop on High Performance Graph Processing","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74376712","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}
Yuichiro Yasui, K. Fujisawa, E. L. Goh, John Baron, A. Sugiura, Takashi Uchiyama
Breadth-first search (BFS) is one of the most fundamental processing algorithms in graph theory. We previously presented a scalable BFS algorithm based on Beamer's direction-optimizing algorithm for non-uniform memory access (NUMA)-based systems, in which the NUMA architecture was carefully considered. This paper presents our new implementation that reduces remote memory access in a top-down direction of direction-optimizing algorithm. We also discuss numerical results obtained on the SGI UV 2000 and UV 300 systems, which are shared-memory supercomputers based on a cache coherent (cc)-NUMA architecture that can handle thousands of threads on a single operating system. Our implementation has achieved performance rates of 219 billion edges per second on a Kronecker graph with 234 vertices and 238 edges on a rack of an SGI UV 300 system with 1,152 threads. This result exceeds the fastest entry for a shared-memory system on the current Graph500 list presented in November 2015, which includes our previous implementation.
{"title":"NUMA-aware Scalable Graph Traversal on SGI UV Systems","authors":"Yuichiro Yasui, K. Fujisawa, E. L. Goh, John Baron, A. Sugiura, Takashi Uchiyama","doi":"10.1145/2915516.2915522","DOIUrl":"https://doi.org/10.1145/2915516.2915522","url":null,"abstract":"Breadth-first search (BFS) is one of the most fundamental processing algorithms in graph theory. We previously presented a scalable BFS algorithm based on Beamer's direction-optimizing algorithm for non-uniform memory access (NUMA)-based systems, in which the NUMA architecture was carefully considered. This paper presents our new implementation that reduces remote memory access in a top-down direction of direction-optimizing algorithm. We also discuss numerical results obtained on the SGI UV 2000 and UV 300 systems, which are shared-memory supercomputers based on a cache coherent (cc)-NUMA architecture that can handle thousands of threads on a single operating system. Our implementation has achieved performance rates of 219 billion edges per second on a Kronecker graph with 234 vertices and 238 edges on a rack of an SGI UV 300 system with 1,152 threads. This result exceeds the fastest entry for a shared-memory system on the current Graph500 list presented in November 2015, which includes our previous implementation.","PeriodicalId":20568,"journal":{"name":"Proceedings of the ACM Workshop on High Performance Graph Processing","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90413134","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}
Recently, distributed processing of large dynamic graphs has become very popular, especially in certain domains such as social network analysis, Web graph analysis and spatial network analysis. In this context, many distributed/parallel graph processing systems have been proposed, such as Pregel, GraphLab, and Trinity. These systems can be divided into two categories: (1) vertex-centric and (2) block-centric approaches. In vertex-centric approaches, each vertex corresponds to a process, and message are exchanged among vertices. In block-centric approaches, the unit of computation is a block, a connected subgraph of the graph, and message exchanges occur among blocks. In this paper, we are considering the issues of scale and dynamism in the case of block-centric approaches. We present BLADYG, a block-centric framework that addresses the issue of dynamism in large-scale graphs. We present an implementation of BLADYG on top of AKKA framework. We experimentally evaluate the performance of the proposed framework.
{"title":"BLADYG: A Novel Block-Centric Framework for the Analysis of Large Dynamic Graphs","authors":"Sabeur Aridhi, A. Montresor, Yannis Velegrakis","doi":"10.1145/2915516.2915525","DOIUrl":"https://doi.org/10.1145/2915516.2915525","url":null,"abstract":"Recently, distributed processing of large dynamic graphs has become very popular, especially in certain domains such as social network analysis, Web graph analysis and spatial network analysis. In this context, many distributed/parallel graph processing systems have been proposed, such as Pregel, GraphLab, and Trinity. These systems can be divided into two categories: (1) vertex-centric and (2) block-centric approaches. In vertex-centric approaches, each vertex corresponds to a process, and message are exchanged among vertices. In block-centric approaches, the unit of computation is a block, a connected subgraph of the graph, and message exchanges occur among blocks. In this paper, we are considering the issues of scale and dynamism in the case of block-centric approaches. We present BLADYG, a block-centric framework that addresses the issue of dynamism in large-scale graphs. We present an implementation of BLADYG on top of AKKA framework. We experimentally evaluate the performance of the proposed framework.","PeriodicalId":20568,"journal":{"name":"Proceedings of the ACM Workshop on High Performance Graph Processing","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78410022","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}
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