2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)最新文献_第3页

Localized Fault Recovery for Nested Fork-Join Programs 嵌套Fork-Join程序的局部故障恢复

2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS) Pub Date : 2017-05-01 DOI: 10.1109/IPDPS.2017.75

Gokcen Kestor, S. Krishnamoorthy, Wenjing Ma

引用次数: 18

Design and Implementation of Papyrus: Parallel Aggregate Persistent Storage Papyrus的设计与实现:并行聚合持久存储

2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS) Pub Date : 2017-05-01 DOI: 10.1109/IPDPS.2017.72

Jungwon Kim, Kittisak Sajjapongse, Seyong Lee, J. Vetter

{"title":"Design and Implementation of Papyrus: Parallel Aggregate Persistent Storage","authors":"Jungwon Kim, Kittisak Sajjapongse, Seyong Lee, J. Vetter","doi":"10.1109/IPDPS.2017.72","DOIUrl":"https://doi.org/10.1109/IPDPS.2017.72","url":null,"abstract":"A surprising development in recently announced HPC platforms is the addition of, sometimes massive amounts of, persistent (nonvolatile) memory (NVM) in order to increase memory capacity and compensate for plateauing I/O capabilities. However, there are no portable and scalable programming interfaces using aggregate NVM effectively. This paper introduces Papyrus: a new software system built to exploit emerging capability of NVM in HPC architectures. Papyrus (or Parallel Aggregate Persistent -YRU- Storage) is a novel programming system that provides features for scalable, aggregate, persistent memory in an extreme-scale system for typical HPC usage scenarios. Papyrus mainly consists of Papyrus Virtual File System (VFS) and Papyrus Template Container Library (TCL). Papyrus VFS provides a uniform aggregate NVM storage image across diverse NVM architectures. It enables Papyrus TCL to provide a portable and scalable high-level container programming interface whose data elements are distributed across multiple NVM nodes without requiring the user to handle complex communication, synchronization, replication, and consistency model. We evaluate Papyrus on two HPC systems, including UTK Beacon and NERSC Cori, using real NVM storage devices.","PeriodicalId":209524,"journal":{"name":"2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131848758","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

HOMP: Automated Distribution of Parallel Loops and Data in Highly Parallel Accelerator-Based Systems 高度并行加速器系统中并行环路和数据的自动分布

2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS) Pub Date : 2017-05-01 DOI: 10.1109/IPDPS.2017.99

Yonghong Yan, Jiawen Liu, K. Cameron, M. Umar

引用次数: 6

Addressing Performance Heterogeneity in MapReduce Clusters with Elastic Tasks 使用弹性任务解决MapReduce集群的性能异构问题

2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS) Pub Date : 2017-05-01 DOI: 10.1109/IPDPS.2017.28

Wei Chen, J. Rao, Xiaobo Zhou

引用次数: 11

NVIDIA Deep Learning Tutorial NVIDIA深度学习教程

2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS) Pub Date : 2017-05-01 DOI: 10.1109/IPDPS.2017.7

J. Bernauer

引用次数: 1

Container-Based Cloud Platform for Mobile Computation Offloading 基于容器的移动计算卸载云平台

2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS) Pub Date : 2017-05-01 DOI: 10.1109/IPDPS.2017.47

Song Wu, Chao Niu, J. Rao, Hai Jin, Xiaohai Dai

{"title":"Container-Based Cloud Platform for Mobile Computation Offloading","authors":"Song Wu, Chao Niu, J. Rao, Hai Jin, Xiaohai Dai","doi":"10.1109/IPDPS.2017.47","DOIUrl":"https://doi.org/10.1109/IPDPS.2017.47","url":null,"abstract":"With the explosive growth of smartphones and cloud computing, mobile cloud, which leverages cloud resource to boost the performance of mobile applications, becomes attrac- tive. Many efforts have been made to improve the performance and reduce energy consumption of mobile devices by offloading computational codes to the cloud. However, the offloading cost caused by the cloud platform has been ignored for many years. In this paper, we propose Rattrap, a lightweight cloud platform which improves the offloading performance from cloud side. To achieve such goals, we analyze the characteristics of typical of- floading workloads and design our platform solution accordingly. Rattrap develops a new runtime environment, Cloud Android Container, for mobile computation offloading, replacing heavy- weight virtual machines (VMs). Our design exploits the idea of running operating systems with differential kernel features inside containers with driver extensions, which partially breaks the limitation of OS-level virtualization. With proposed resource sharing and code cache mechanism, Rattrap fundamentally improves the offloading performance. Our evaluation shows that Rattrap not only reduces the startup time of runtime environments and shows an average speedup of 16x, but also saves a large amount of system resources such as 75% memory footprint and at least 79% disk capacity. Moreover, Rattrap improves offloading response by as high as 63% over the cloud platform based on VM, and thus saving the battery life.","PeriodicalId":209524,"journal":{"name":"2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)","volume":"34 13","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132973209","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: 47

Application Level Reordering of Remote Direct Memory Access Operations 远程直接内存访问操作的应用程序级重新排序

2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS) Pub Date : 2017-05-01 DOI: 10.1109/IPDPS.2017.98

W. Lavrijsen, Costin Iancu

引用次数: 6

E^2MC: Entropy Encoding Based Memory Compression for GPUs 基于熵编码的gpu内存压缩

2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS) Pub Date : 2017-05-01 DOI: 10.1109/IPDPS.2017.101

S. Lal, J. Lucas, B. Juurlink

{"title":"E^2MC: Entropy Encoding Based Memory Compression for GPUs","authors":"S. Lal, J. Lucas, B. Juurlink","doi":"10.1109/IPDPS.2017.101","DOIUrl":"https://doi.org/10.1109/IPDPS.2017.101","url":null,"abstract":"Modern Graphics Processing Units (GPUs) provide much higher off-chip memory bandwidth than CPUs, but many GPU applications are still limited by memory bandwidth. Unfortunately, off-chip memory bandwidth is growing slower than the number of cores and has become a performance bottleneck. Thus, optimizations of effective memory bandwidth play a significant role for scaling the performance of GPUs. Memory compression is a promising approach for improving memory bandwidth which can translate into higher performance and energy efficiency. However, compression is not free and its challenges need to be addressed, otherwise the benefits of compression may be offset by its overhead. We propose an entropy encoding based memory compression (E2MC) technique for GPUs, which is based on the well-known Huffman encoding. We study the feasibility of entropy encoding for GPUs and show that it achieves higher compression ratios than state-of-the-art GPU compression techniques. Furthermore, we address the key challenges of probability estimation, choosing an appropriate symbol length for encoding, and decompression with low latency. The average compression ratio of E2MC is 53% higher than the state of the art. This translates into an average speedup of 20% compared to no compression and 8% higher compared to the state of the art. Energy consumption and energy-delayproduct are reduced by 13% and 27%, respectively. Moreover, the compression ratio achieved by E2MC is close to the optimal compression ratio given by Shannon’s source coding theorem.","PeriodicalId":209524,"journal":{"name":"2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123194738","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: 20

A Scalable System Architecture to Addressing the Next Generation of Predictive Simulation Workflows with Coupled Compute and Data Intensive Applications 一个可扩展的系统架构，以解决与耦合计算和数据密集型应用程序的下一代预测仿真工作流

2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS) Pub Date : 2017-05-01 DOI: 10.1109/IPDPS.2017.129

M. Seager

引用次数: 0

MetaKV: A Key-Value Store for Metadata Management of Distributed Burst Buffers MetaKV:用于分布式突发缓冲区元数据管理的键值存储

2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS) Pub Date : 2017-05-01 DOI: 10.1109/IPDPS.2017.39

Teng Wang, A. Moody, Yue Zhu, K. Mohror, Kento Sato, T. Islam, Weikuan Yu

{"title":"MetaKV: A Key-Value Store for Metadata Management of Distributed Burst Buffers","authors":"Teng Wang, A. Moody, Yue Zhu, K. Mohror, Kento Sato, T. Islam, Weikuan Yu","doi":"10.1109/IPDPS.2017.39","DOIUrl":"https://doi.org/10.1109/IPDPS.2017.39","url":null,"abstract":"Distributed burst buffers are a promising storage architecture for handling I/O workloads for exascale computing. Their aggregate storage bandwidth grows linearly with system node count. However, although scientific applications can achieve scalable write bandwidth by having each process write to its node-local burst buffer, metadata challenges remain formidable, especially for files shared across many processes. This is due to the need to track and organize file segments across the distributed burst buffers in a global index. Because this global index can be accessed concurrently by thousands or more processes in a scientific application, the scalability of metadata management is a severe performance-limiting factor. In this paper, we propose MetaKV: a key-value store that provides fast and scalable metadata management for HPC metadata workloads on distributed burst buffers. MetaKV complements the functionality of an existing key-value store with specialized metadata services that efficiently handle bursty and concurrent metadata workloads: compressed storage management, supervised block clustering, and log-ring based collective message reduction. Our experiments demonstrate that MetaKV outperforms the state-of-the-art key-value stores by a significant margin. It improves put and get metadata operations by as much as 2.66× and 6.29×, respectively, and the benefits of MetaKV increase with increasing metadata workload demand.","PeriodicalId":209524,"journal":{"name":"2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129041877","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: 17