2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS)最新文献_第4页

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2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS) Pub Date : 2020-05-01 DOI: 10.1109/ipdps47924.2020.00002

引用次数: 0

Adaptive Page Migration for Irregular Data-intensive Applications under GPU Memory Oversubscription GPU内存超订阅下不规则数据密集型应用的自适应页面迁移

2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS) Pub Date : 2020-05-01 DOI: 10.1109/IPDPS47924.2020.00054

D. Ganguly, Ziyu Zhang, Jun Yang, R. Melhem

{"title":"Adaptive Page Migration for Irregular Data-intensive Applications under GPU Memory Oversubscription","authors":"D. Ganguly, Ziyu Zhang, Jun Yang, R. Melhem","doi":"10.1109/IPDPS47924.2020.00054","DOIUrl":"https://doi.org/10.1109/IPDPS47924.2020.00054","url":null,"abstract":"Unified Memory in heterogeneous systems serves a wide range of applications. However, limited capacity of the device memory becomes a first order performance bottleneck for data-intensive general-purpose applications with increasing working sets. The performance overhead under memory oversubscription depends on the memory access pattern of the corresponding workload. While a regular application with sequential, dense memory access suffers from long latency write-backs, performance of a irregular application with sparse, seldom access to large data-sets degrades due to page thrashing. Although smart spatio-temporal prefetching and large page eviction yield good performance in general, remote zero-copy access to host-pinned memory proves to be beneficial for irregular, data-intensive applications. Further, new generation GPUs introduced hardware access counters to delay page migration and reduce memory thrashing. However, the responsibility of deciding what strategy is the best fit for a given application relies heavily on the programmer based on thorough understanding of the memory access pattern through intrusive profiling. In this work, we propose a programmer-agnostic runtime that leverages the hardware access counters to automatically categorize memory allocations based on the access pattern and frequency. The proposed heuristic adaptively navigates between remote zero-copy access to host-pinned memory and first-touch page migration based on the trade-off between low latency remote access and high-bandwidth local access. We show that although designed to address memory oversubscription, our scheme has no impact on performance when working sets fit in the device-local memory. Experimental results show that our scheme provides performance improvement of 22% to 78% for irregular applications under 125% memory oversubscription compared to the state of the art. At the same time, regular applications are not impacted by the framework.","PeriodicalId":6805,"journal":{"name":"2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS)","volume":"50 1","pages":"451-461"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73609856","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: 30

Neksus: An Interconnect for Heterogeneous System-In-Package Architectures Neksus:异构系统包架构的互连

2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS) Pub Date : 2020-05-01 DOI: 10.1109/IPDPS47924.2020.00012

Vidushi Goyal, Xiaowei Wang, V. Bertacco, R. Das

引用次数: 2

EC-Fusion: An Efficient Hybrid Erasure Coding Framework to Improve Both Application and Recovery Performance in Cloud Storage Systems EC-Fusion:一种有效的混合擦除编码框架，以提高云存储系统中的应用和恢复性能

2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS) Pub Date : 2020-05-01 DOI: 10.1109/IPDPS47924.2020.00029

Han Qiu, Chentao Wu, Jie Li, M. Guo, Tong Liu, Xubin He, Yuanyuan Dong, Yafei Zhao

{"title":"EC-Fusion: An Efficient Hybrid Erasure Coding Framework to Improve Both Application and Recovery Performance in Cloud Storage Systems","authors":"Han Qiu, Chentao Wu, Jie Li, M. Guo, Tong Liu, Xubin He, Yuanyuan Dong, Yafei Zhao","doi":"10.1109/IPDPS47924.2020.00029","DOIUrl":"https://doi.org/10.1109/IPDPS47924.2020.00029","url":null,"abstract":"Nowadays erasure coding is one of the most significant techniques in cloud storage systems, which provides both quick parallel I/O processing and high capabilities of fault tolerance on massive data accesses. In these systems, triple disk failure tolerant arrays (3DFTs) is a typical configuration, which is supported by several classic erasure codes like Reed-Solomon (RS) codes, Local Reconstruction Codes (LRC), Minimum Storage Regeneration (MSR) codes, etc. For an online recovery process, the foreground application workloads and the background recovery workloads are handled simultaneously, which requires a comprehensive understanding on both two types of workload characteristics. Although several techniques have been proposed to accelerate the I/O requests of online recovery processes, they are typically unilateral due to the fact that the above two workloads are not combined together to achieve high cost-effective performance.To address this problem, we propose Erasure Codes Fusion (EC-Fusion), an efficient hybrid erasure coding framework in cloud storage systems. EC-Fusion is a combination of RS and MSR codes, which dynamically selects the appropriate code based on its properties. On one hand, for write-intensive application workloads or low risk on data loss in recovery workloads, EC-Fusion uses RS code to decrease the computational overhead and storage cost concurrently. On the other hand, for read-intensive or frequent reconstruction in workloads, MSR code is a proper choice. Therefore, a better overall application and recovery performance can be achieved in a cost-effective fashion. To demonstrate the effectiveness of EC-Fusion, several experiments are conducted in hadoop systems. The results show that, compared with the traditional hybrid erasure coding techniques, EC-Fusion accelerates the response time for application by up to 1.77×, and reduces the reconstruction time by up to 69.10%.","PeriodicalId":6805,"journal":{"name":"2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS)","volume":"16 1","pages":"191-201"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87324613","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

SCSL: Optimizing Matching Algorithms to Improve Real-time for Content-based Pub/Sub Systems 优化匹配算法以提高基于内容的Pub/Sub系统的实时性

2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS) Pub Date : 2020-05-01 DOI: 10.1109/IPDPS47924.2020.00025

Tianchen Ding, Shiyou Qian, Jian Cao, Guangtao Xue, Minglu Li

{"title":"SCSL: Optimizing Matching Algorithms to Improve Real-time for Content-based Pub/Sub Systems","authors":"Tianchen Ding, Shiyou Qian, Jian Cao, Guangtao Xue, Minglu Li","doi":"10.1109/IPDPS47924.2020.00025","DOIUrl":"https://doi.org/10.1109/IPDPS47924.2020.00025","url":null,"abstract":"Although many matching algorithms have been proposed to improve the matching efficiency of the content-based publish/subscribe system, existing work seldom consider the real-time of event dissemination from the perspective of event matching. On the basis of two existing matching algorithms, in this paper, we propose a subscription-classifying and structure-layering (SCSL) optimization method for matching algorithms, aiming to improve real-time by shortening the determining time of matching subscriptions. The basic idea of SCSL is that subscriptions with high matching probabilities should be processed first in the process of event matching and their storage positions in the data structure should be adjusted in line with changing probabilities. One challenge of SCSL is the trade-off that needs to be made between the gains of improving real-time performance by identifying matching subscriptions earlier and the cost of increasing matching time due to subscription classification and adjustment. We design a concise scheme to classify subscriptions, establish a lightweight adjustment mechanism to deal with dynamics and propose an efficient greedy algorithm to compute the adjustment solution, which alleviates the impact of SCSL on matching performance. The experiment results show that the 95th percentile of the determining time of matching subscriptions is improved by about 70%. Furthermore, we integrate SCSL into Apache Kafka to augment it as a content-based publish/subscribe system and test the effect of SCSL based on real-world stock trace data, which witnesses about 40% improvement on the average event transfer latency and confirms that SCSL can effectively improve the real-time performance of content-based publish/subscribe systems.","PeriodicalId":6805,"journal":{"name":"2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS)","volume":"85 1","pages":"148-157"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85613194","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: 5

Optimize Scheduling of Federated Learning on Battery-powered Mobile Devices 电池供电移动设备上联邦学习的优化调度

2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS) Pub Date : 2020-05-01 DOI: 10.1109/IPDPS47924.2020.00031

Cong Wang, Xin Wei, Pengzhan Zhou

{"title":"Optimize Scheduling of Federated Learning on Battery-powered Mobile Devices","authors":"Cong Wang, Xin Wei, Pengzhan Zhou","doi":"10.1109/IPDPS47924.2020.00031","DOIUrl":"https://doi.org/10.1109/IPDPS47924.2020.00031","url":null,"abstract":"Federated learning learns a collaborative model by aggregating locally-computed updates from mobile devices for privacy preservation. While current research typically prioritizing the minimization of communication overhead, we demonstrate from an empirical study, that computation heterogeneity is a more pronounced bottleneck on battery-powered mobile devices. Moreover, if class is unbalanced among the mobile devices, inappropriate selection of participants may adversely cause gradient divergence and accuracy loss. In this paper, we utilize data as a tunable knob to schedule training and achieve near-optimal solutions of computation time and accuracy loss. Based on the offline profiling, we formulate optimization problems and propose polynomial-time algorithms when data is class-balanced or unbalanced. We evaluate the optimization framework extensively on a mobile testbed with two datasets. Compared with common benchmarks of federated learning, our algorithms achieve 210× speedups with negligible accuracy loss. They also mitigate the impact from mobile stragglers and improve parallelism for federated learning.","PeriodicalId":6805,"journal":{"name":"2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS)","volume":"6 1","pages":"212-221"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87961294","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: 16

Dynamic Graphs on the GPU GPU上的动态图形

2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS) Pub Date : 2020-05-01 DOI: 10.1109/IPDPS47924.2020.00081

Muhammad A. Awad, Saman Ashkiani, Serban D. Porumbescu, John Douglas Owens

引用次数: 16

Fault-Tolerant Containers Using NiLiCon 使用NiLiCon的容错容器

2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS) Pub Date : 2020-05-01 DOI: 10.1109/IPDPS47924.2020.00114

Diyu Zhou, Y. Tamir

{"title":"Fault-Tolerant Containers Using NiLiCon","authors":"Diyu Zhou, Y. Tamir","doi":"10.1109/IPDPS47924.2020.00114","DOIUrl":"https://doi.org/10.1109/IPDPS47924.2020.00114","url":null,"abstract":"Many services deployed in the cloud require high reliability and must thus survive machine failures. Providing such fault tolerance transparently, without requiring application modifications, has motivated extensive research on replicating virtual machines (VMs). Cloud computing typically relies on VMs or containers to provide an isolation and multitenancy layer. Containers have advantages over VMs in smaller size, faster startup, and avoiding the need to manage updates of multiple VMs. This paper reports on the design, implementation, and evaluation of NiLiCon — a transparent container replication mechanism for fault tolerance. To the best of our knowledge, NiLiCon is the first implementation of container replication, demonstrating that it can be used for transparent deployment of critical services in the cloud.NiLiCon is based on high-frequency asynchronous incremental checkpointing to a warm spare, as previously used for VMs. The challenge to accomplishing this is that, compared to VMs, there is much tighter coupling between the container state and the state of the underlying platform. NiLiCon meets this challenge, eliminating the need to deploy services in VMs, with performance overheads that are competitive with those of similar VM replication mechanisms. Specifically, with the seven benchmarks used in the evaluation, the performance overhead of NiLiCon is in the range of 19%-67%. For fail-stop faults, the recovery rate is 100%.","PeriodicalId":6805,"journal":{"name":"2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS)","volume":"19 1","pages":"1082-1091"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74983245","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: 2

Efficient Parallel and Adaptive Partitioning for Load-balancing in Spatial Join 空间连接中负载均衡的高效并行和自适应分区

2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS) Pub Date : 2020-05-01 DOI: 10.1109/IPDPS47924.2020.00088

Jie Yang, S. Puri

{"title":"Efficient Parallel and Adaptive Partitioning for Load-balancing in Spatial Join","authors":"Jie Yang, S. Puri","doi":"10.1109/IPDPS47924.2020.00088","DOIUrl":"https://doi.org/10.1109/IPDPS47924.2020.00088","url":null,"abstract":"Due to the developments of topographic techniques, clear satellite imagery, and various means for collecting information, geospatial datasets are growing in volume, complexity, and heterogeneity. For efficient execution of spatial computations and analytics on large spatial data sets, parallel processing is required. To exploit fine-grained parallel processing in large scale compute clusters, partitioning in a load-balanced way is necessary for skewed datasets. In this work, we focus on spatial join operation where the inputs are two layers of geospatial data. Our partitioning method for spatial join uses Adaptive Partitioning (ADP) technique, which is based on Quadtree partitioning. Unlike existing partitioning techniques, ADP partitions the spatial join workload instead of partitioning the individual datasets separately to provide better load-balancing. Based on our experimental evaluation, ADP partitions spatial data in a more balanced way than Quadtree partitioning and Uniform grid partitioning. ADP uses an output-sensitive duplication avoidance technique which minimizes duplication of geometries that are not part of spatial join output. In a distributed memory environment, this technique can reduce data communication and storage requirements compared to traditional methods.To improve the performance of ADP, an MPI+Threads based parallelization is presented. With ParADP, a pair of real world datasets, one with 717 million polylines and another with 10 million polygons, is partitioned into 65,536 grid cells within 7 seconds. ParADP performs well with both good weak scaling up to 4,032 CPU cores and good strong scaling up to 4,032 CPU cores.","PeriodicalId":6805,"journal":{"name":"2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS)","volume":"394 1","pages":"810-820"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80246543","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: 7

Amoeba: QoS-Awareness and Reduced Resource Usage of Microservices with Serverless Computing 变形虫:无服务器计算微服务的qos意识和减少资源使用

2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS) Pub Date : 2020-05-01 DOI: 10.1109/IPDPS47924.2020.00049

Zijun Li, Quan Chen, Shuai Xue, Tao Ma, Yong Yang, Zhuo Song, M. Guo

{"title":"Amoeba: QoS-Awareness and Reduced Resource Usage of Microservices with Serverless Computing","authors":"Zijun Li, Quan Chen, Shuai Xue, Tao Ma, Yong Yang, Zhuo Song, M. Guo","doi":"10.1109/IPDPS47924.2020.00049","DOIUrl":"https://doi.org/10.1109/IPDPS47924.2020.00049","url":null,"abstract":"While microservices that have stringent Quality-of-Service constraints are deployed in the Clouds, the long-term rented infrastructures that host the microservices are under-utilized except peak hours due to the diurnal load pattern. It is resource efficient for Cloud vendors and cost efficient for service maintainers to deploy the microservices in the long-term infrastructure at high load and in the serverless computing platform at low load. However, prior work fails to take advantage of the opportunity, because the contention between microservices on the serverless platform seriously affects their response latencies.Our investigation shows that the load of a microservice, the shared resource contentions on the serverless platform, and its sensitivities to the contention together affect the response latency of the microservice on the platform. To this end, we propose Amoeba, a runtime system that dynamically switches the deployment of a microservice. Amoeba is comprised of a contention-aware deployment controller, a hybrid execution engine, and a multi-resource contention monitor. The deployment controller predicts the tail latency of a microservice based on its load and the contention on the serverless platform, and determines the appropriate deployment of the microservice. The hybrid execution engine enables the quick switch of the two deploy modes. The contention monitor periodically quantifies the contention on multiple types of shared resources. Experimental results show that Amoeba is able to significantly reduce up to 72.9% of CPU usage and up to 84.9% of memory usage compared with the traditional pure IaaS-based deployment, while ensuring the required latency target.","PeriodicalId":6805,"journal":{"name":"2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS)","volume":"156 1","pages":"399-408"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73732779","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: 14