2014 21st International Conference on High Performance Computing (HiPC)最新文献_第5页

Premonition of storage response class using Skyline ranked Ensemble method 采用Skyline排序集成法预测存储响应等级

2014 21st International Conference on High Performance Computing (HiPC) Pub Date : 2014-12-01 DOI: 10.1109/HiPC.2014.7116886

K. Dheenadayalan, V. Muralidhara, Pushpa Datla, G. Srinivasaraghavan, Maulik Shah

引用次数: 4

A flexible scheduling framework for heterogeneous CPU-GPU clusters 灵活的CPU-GPU异构集群调度框架

2014 21st International Conference on High Performance Computing (HiPC) Pub Date : 2014-12-01 DOI: 10.1109/HiPC.2014.7116892

Kittisak Sajjapongse, T. Agarwal, M. Becchi

{"title":"A flexible scheduling framework for heterogeneous CPU-GPU clusters","authors":"Kittisak Sajjapongse, T. Agarwal, M. Becchi","doi":"10.1109/HiPC.2014.7116892","DOIUrl":"https://doi.org/10.1109/HiPC.2014.7116892","url":null,"abstract":"In the last few years, thanks to their computational power and progressively increased programmability, GPUs have become part of HPC clusters. As a result, widely used open-source cluster resource managers (e.g. TORQUE and SLURM) have recently been extended with GPU support capabilities. These systems, however, treat GPUs as dedicated resources and provide scheduling mechanisms that often result in resource underutilization and, thereby, in suboptimal performance. We propose a cluster-level scheduler and integrate it with our previously proposed node-level GPU virtualization runtime [1, 2], thus providing a hierarchical cluster resource management framework that allows the efficient use of heterogeneous CPU-GPU clusters. The scheduling policy used by our system is configurable, and our scheduler provides administrators with a high-level API that allows easily defining custom scheduling policies. We provide two application- and hardware-heterogeneity-aware cluster-level scheduling schemes for hybrid MPI-CUDA applications: co-location- and latency-reduction-based scheduling, and use them in combination with a preemption-based GPU sharing policy implemented at the node-level. We validate our framework on two heterogeneous clusters: one consisting of commodity workstations and the other of high-end nodes with various hardware configurations, and on a mix of communication- and compute-intensive applications. Our experiments show that, by better utilizing the available resources, our scheduling framework outperforms existing batch-schedulers both in terms of throughput and application latency.","PeriodicalId":337777,"journal":{"name":"2014 21st International Conference on High Performance Computing (HiPC)","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116466397","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: 4

Improving Multi-dimensional query processing with data migration in distributed cache infrastructure 利用数据迁移改进分布式缓存基础设施中的多维查询处理

2014 21st International Conference on High Performance Computing (HiPC) Pub Date : 2014-12-01 DOI: 10.1109/HiPC.2014.7116906

Youngmoon Eom, Jinwoong Kim, Deukyeon Hwang, J. Kwak, Minho Shin, Beomseok Nam

{"title":"Improving Multi-dimensional query processing with data migration in distributed cache infrastructure","authors":"Youngmoon Eom, Jinwoong Kim, Deukyeon Hwang, J. Kwak, Minho Shin, Beomseok Nam","doi":"10.1109/HiPC.2014.7116906","DOIUrl":"https://doi.org/10.1109/HiPC.2014.7116906","url":null,"abstract":"In distributed query processing systems where caching infrastructure is distributed and scales with the number of servers, it is becoming more important to orchestrate and leverage a large number of cached objects in distributed caching systems seamlessly as the present trend is to build large scalable distributed systems by connecting small heterogeneous machines. With a large scale distributed caching system, a scheduling policy must consider both cache hit ratio and system load balance to optimize multiple queries. A scheduling policy that considers system load but not cache hit ratio often fails to reuse cached data by not assigning a query to the sever that has data objects the query needs. On the contrary, a scheduling policy that considers cache hit ratio but not system load balance may suffer from system load imbalance. To maximize the overall system throughput and to reduce query response time, a multiple query scheduling policy must balance system load and also leverage cached objects. In this paper, we present a distributed query processing framework that exhibits high cache hit ratio while achieving good system load balance. In order to seamlessly manage our distributed scalable caching system, our framework performs autonomic cached data migrations to improve cache hit ratio. Our experiments show that our proposed query scheduling policy and data migration policy significantly improve system throughput by achieving high cache hit ratio while avoiding system load imbalance.","PeriodicalId":337777,"journal":{"name":"2014 21st International Conference on High Performance Computing (HiPC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127920387","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

Coupling-aware graph partitioning algorithms: Preliminary study 耦合感知图划分算法:初步研究

2014 21st International Conference on High Performance Computing (HiPC) Pub Date : 2014-12-01 DOI: 10.1109/HiPC.2014.7116879

Maria Predari, Aurélien Esnard

引用次数: 5

A proactive approach for coping with uncertain resource availabilities on desktop grids 一种在桌面网格上处理不确定资源可用性的主动方法

2014 21st International Conference on High Performance Computing (HiPC) Pub Date : 2014-05-29 DOI: 10.1109/HiPC.2014.7116890

Louis-Claude Canon, Adel Essafi, D. Trystram

引用次数: 7

Efficient and robust allocation algorithms in clouds under memory constraints 内存约束下的高效鲁棒云分配算法

2014 21st International Conference on High Performance Computing (HiPC) Pub Date : 2013-10-19 DOI: 10.1109/HiPC.2014.7116894

Olivier Beaumont, J. Lorenzo, Lionel Eyraud-Dubois, Paul Renaud-Goud

{"title":"Efficient and robust allocation algorithms in clouds under memory constraints","authors":"Olivier Beaumont, J. Lorenzo, Lionel Eyraud-Dubois, Paul Renaud-Goud","doi":"10.1109/HiPC.2014.7116894","DOIUrl":"https://doi.org/10.1109/HiPC.2014.7116894","url":null,"abstract":"We consider robust resource allocation of services in Clouds. More specifically, we consider the case of a large public or private Cloud platform such that a relatively small set of large and independent services accounts for most of the overall CPU usage of the platform. We will show, using a recent trace from Google, that this assumption is very reasonable in practice. The objective is to provide an allocation of the services onto the machines of the platform, using replication in order to be resilient to machine failures. The services are characterized by their demand along several dimensions (CPU, memory,...) and by their quality of service requirements, that have been defined through an SLA in the case of a public Cloud or fixed by the administrator in the case of a private Cloud. This quality of service defines the required robustness of the service, by setting an upper limit on the probability that the provider fails to allocate the required quantity of resources. This maximum probability of failure can be transparently turned into a set of (price, penalty) pairs. Our contribution is two-fold. First, we propose a formal model for this allocation problem, and we justify our assumptions based on an analysis of a publicly available cluster usage trace from Google. Second, we propose a resource allocation strategy whose complexity is low in the number of resources, what makes it well suited to large platforms. Finally, we provide an analysis of the proposed strategy through an extensive set of simulations, showing that it can be succesfully applied in the context of the Google trace.","PeriodicalId":337777,"journal":{"name":"2014 21st International Conference on High Performance Computing (HiPC)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128189982","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: 4

Towards realizing the potential of malleable jobs 实现可塑工作的潜力

2014 21st International Conference on High Performance Computing (HiPC) Pub Date : 1900-01-01 DOI: 10.1109/HiPC.2014.7116905

Abhishek K. Gupta, Bilge Acun, O. Sarood, L. Kalé

{"title":"Towards realizing the potential of malleable jobs","authors":"Abhishek K. Gupta, Bilge Acun, O. Sarood, L. Kalé","doi":"10.1109/HiPC.2014.7116905","DOIUrl":"https://doi.org/10.1109/HiPC.2014.7116905","url":null,"abstract":"Malleable jobs are those which can dynamically shrink or expand the number of processors on which they are executing at runtime in response to an external command. Malleable jobs can significantly improve system utilization and reduce average response time, compared to traditional jobs. To realize these benefits, three components are critical - an adaptive job scheduler, an adaptive resource manager, and an adaptive parallel runtime system. In this paper, we present a novel mechanism for enabling shrink/expand capability in the parallel runtime system using task migration and dynamic load balancing, checkpoint-restart, and Linux shared memory. Our technique performs true shrink/expand eliminating the need of any residual processes, requires little application programmer effort, and is fast. Further, we establish a bidirectional communication channel between the resource manager and the parallel runtime, and present an asynchronous split-phase mechanism for executing adaptive scheduling decisions. Performance results using Charm++ on Stampede supercomputer show the efficacy, scalability, and benefits of our approach. Shrinking from 2k to 1k cores takes 16s while expand from 1k to 2k takes 40s. Also, we demonstrate the utility of our runtime in traditional as well as emerging scenarios, e.g., proactive fault tolerance and clouds.","PeriodicalId":337777,"journal":{"name":"2014 21st International Conference on High Performance Computing (HiPC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124387334","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: 39

Optimizing the performance of parallel applications on a 5D torus via task mapping 通过任务映射优化5D环面上并行应用程序的性能

2014 21st International Conference on High Performance Computing (HiPC) Pub Date : 1900-01-01 DOI: 10.1109/HiPC.2014.7116706

A. Bhatele, Nikhil Jain, Katherine E. Isaacs, Ronak Buch, T. Gamblin, S. Langer, L. Kalé

引用次数: 18

Analysis and tuning of libtensor framework on multicore architectures libtensor框架在多核架构下的分析与调优

2014 21st International Conference on High Performance Computing (HiPC) Pub Date : 1900-01-01 DOI: 10.1109/HIPC.2014.7116881

K. Ibrahim, Samuel Williams, E. Epifanovsky, A. Krylov

引用次数: 12