Proceedings of the 25th ACM International Symposium on High-Performance Parallel and Distributed Computing最新文献_第6页

Preparing for Supercomputing's Sixth Wave 为超级计算的第六次浪潮做准备

Proceedings of the 25th ACM International Symposium on High-Performance Parallel and Distributed Computing Pub Date : 2016-05-31 DOI: 10.1145/2907294.2911994

J. Vetter

引用次数: 0

Wiera: Towards Flexible Multi-Tiered Geo-Distributed Cloud Storage Instances Wiera:迈向灵活的多层地理分布式云存储实例

Proceedings of the 25th ACM International Symposium on High-Performance Parallel and Distributed Computing Pub Date : 2016-05-31 DOI: 10.1145/2907294.2907322

Kwangsung Oh, A. Chandra, J. Weissman

{"title":"Wiera: Towards Flexible Multi-Tiered Geo-Distributed Cloud Storage Instances","authors":"Kwangsung Oh, A. Chandra, J. Weissman","doi":"10.1145/2907294.2907322","DOIUrl":"https://doi.org/10.1145/2907294.2907322","url":null,"abstract":"Geo-distributed cloud storage systems must tame complexity at many levels: uniform APIs for storage access, supporting flexible storage policies that meet a wide array of application metrics, handling uncertain network dynamics and access dynamism, and operating across many levels of heterogeneity both within and across data-centers. In this paper, we present an integrated solution called Wiera. Wiera extends our earlier cloud storage system, Tiera, that is targeted to multi-tiered policy-based single cloud storage, to the wide-area and multiple data-centers (even across different providers). Wiera enables the specification of global data management policies built on top of local Tiera policies. Such policies enable the user to optimize for cost, performance, reliability, durability, and consistency, both within and across data-centers, and to express their tradeoffs. A key aspect of Wiera is first-class support for dynamism due to network, workload, and access patterns changes. Wiera policies can adapt to changes in user workload, poorly performing data tiers, failures, and changes in user metrics (e.g., cost). Wiera allows unmodified applications to reap the benefits of flexible data/storage policies by externalizing the policy specification. As far as we know, Wiera is the first geo-distributed cloud storage system which handles dynamism actively at run-time. We show how Wiera enables a rich specification of dynamic policies using a concise notation and describe the design and implementation of the system. We have implemented a Wiera prototype on multiple cloud environments, AWS and Azure, that illustrates potential benefits from managing dynamics and in using multiple cloud storage tiers both within and across data-centers.","PeriodicalId":20515,"journal":{"name":"Proceedings of the 25th ACM International Symposium on High-Performance Parallel and Distributed Computing","volume":"74 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75306171","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: 11

Faster and Cheaper: Parallelizing Large-Scale Matrix Factorization on GPUs 更快更便宜:在gpu上并行化大规模矩阵分解

Proceedings of the 25th ACM International Symposium on High-Performance Parallel and Distributed Computing Pub Date : 2016-03-11 DOI: 10.1145/2907294.2907297

Wei Tan, Liangliang Cao, L. Fong

引用次数: 52

Interpretation of Chinese Address Information Based on Multi-factor Inference 基于多因素推理的中文地址信息解释

Proceedings of the 25th ACM International Symposium on High-Performance Parallel and Distributed Computing Pub Date : 2016-01-01 DOI: 10.1109/ISPDC.2016.72

Xiaolin Li, Yanhui Duan, Huabing Zhou, Yi Zhang

引用次数: 0

A Continuous Self-Checking Validation Framework on Processor Exceptions 处理器异常的连续自检验证框架

Proceedings of the 25th ACM International Symposium on High-Performance Parallel and Distributed Computing Pub Date : 2016-01-01 DOI: 10.1109/ISPDC.2016.52

Jian Tan, Daifeng Li

引用次数: 0

Resource Efficiency to Partition Big Streamed Graphs 大流图分区的资源效率

Proceedings of the 25th ACM International Symposium on High-Performance Parallel and Distributed Computing Pub Date : 2015-06-29 DOI: 10.1109/ISPDC.2015.21

Víctor Medel Gracia, Unai Arronategui Arribalzaga

{"title":"Resource Efficiency to Partition Big Streamed Graphs","authors":"Víctor Medel Gracia, Unai Arronategui Arribalzaga","doi":"10.1109/ISPDC.2015.21","DOIUrl":"https://doi.org/10.1109/ISPDC.2015.21","url":null,"abstract":"Real time streaming and processing of big graphs is a relevant and challenging application to be executed in a Cloud infrastructure. We have analysed the amount of resources needed to partition large streamed graphs with different distributed architectures. We have improved state of the art limitations proposing a decentralised and scalable model which is more efficient in memory usage, network traffic and number of processing machines. The improvement has been achieved summarising incoming vertices of the graph and accessing to local information of the already partitioned graph. Classical approaches need all information about the previous vertices. In our system, local information is updated in a feedback scheme periodically. Our experimental results show that current architectures cannot process large scale streamed graphs due to memory limitations. We have proved that our architecture reduces the number of needed machines by seven because it accesses to local memory instead of a distributed one. The total memory size has been also reduced. Finally, our model allows to adjust the quality of the partition solution to the desired amount of memory and network traffic.","PeriodicalId":20515,"journal":{"name":"Proceedings of the 25th ACM International Symposium on High-Performance Parallel and Distributed Computing","volume":"38 1","pages":"120-129"},"PeriodicalIF":0.0,"publicationDate":"2015-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78958158","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

Improving GPU Performance Through Resource Sharing 通过资源共享提升GPU性能

Proceedings of the 25th ACM International Symposium on High-Performance Parallel and Distributed Computing Pub Date : 2015-03-19 DOI: 10.1145/2907294.2907298

Vishwesh Jatala, Jayvant Anantpur, Amey Karkare

{"title":"Improving GPU Performance Through Resource Sharing","authors":"Vishwesh Jatala, Jayvant Anantpur, Amey Karkare","doi":"10.1145/2907294.2907298","DOIUrl":"https://doi.org/10.1145/2907294.2907298","url":null,"abstract":"Graphics Processing Units (GPUs) consisting of Streaming Multiprocessors (SMs) achieve high throughput by running a large number of threads and context switching among them to hide execution latencies. The number of thread blocks, and hence the number of threads that can be launched on an SM, depends on the resource usage--e.g. number of registers, amount of shared memory--of the thread blocks. Since the allocation of threads to an SM is at the thread block granularity, some of the resources may not be used up completely and hence will be wasted. We propose an approach that shares the resources of SM to utilize the wasted resources by launching more thread blocks. We show the effectiveness of our approach for two resources: register sharing, and scratchpad (shared memory) sharing. We further propose optimizations to hide long execution latencies, thus reducing the number of stall cycles. We implemented our approach in GPGPU-Sim simulator and experimentally validated it on 19 applications from 4 different benchmark suites: GPGPU-Sim, Rodinia, CUDA-SDK, and Parboil. We observed that applications that underutilize register resource show a maximum improvement of 24% and an average improvement of 11% with register sharing. Similarly, the applications that underutilize scratchpad resource show a maximum improvement of 30% and an average improvement of 12.5% with scratchpad sharing. The remaining applications, which do not waste any resources, perform similar to the baseline approach.","PeriodicalId":20515,"journal":{"name":"Proceedings of the 25th ACM International Symposium on High-Performance Parallel and Distributed Computing","volume":"89 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2015-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77406955","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: 8

E-NEXT: Network of Excellence - Emerging Network Technologies E-NEXT:卓越网络-新兴网络技术

Proceedings of the 25th ACM International Symposium on High-Performance Parallel and Distributed Computing Pub Date : 2005-07-04 DOI: 10.1109/ISPDC.2005.22

D. Grigoras

引用次数: 1

New Challenges in Parallel Optimization 并行优化中的新挑战

Proceedings of the 25th ACM International Symposium on High-Performance Parallel and Distributed Computing Pub Date : 2005-07-04 DOI: 10.1109/ISPDC.2005.36

E. Alba

引用次数: 0

A New Era in Computing: Moving Services onto Grid 计算的新时代:将服务转移到网格

Proceedings of the 25th ACM International Symposium on High-Performance Parallel and Distributed Computing Pub Date : 2005-07-04 DOI: 10.1109/ISPDC.2005.7

Ian T Foster

引用次数: 3