Nested fork-join programs scheduled using work stealing can automatically balance load and adapt to changes in the execution environment. In this paper, we design an approach to efficiently recover from faults encountered by these programs. Specifically, we focus on localized recovery of the task space in the presence of fail-stop failures. We present an approach to efficiently track, under work stealing, the relationships between the work executed by various threads. This information is used to identify and schedule the tasks to be re-executed without interfering with normal task execution. The algorithm precisely computes the work lost, incurs minimal re-execution overhead, and can recover from an arbitrary number of failures. Experimental evaluation demonstrates low overheads in the absence of failures, recovery overheads on the same order as the lost work, and much lower recovery costs than alternative strategies.
{"title":"Localized Fault Recovery for Nested Fork-Join Programs","authors":"Gokcen Kestor, S. Krishnamoorthy, Wenjing Ma","doi":"10.1109/IPDPS.2017.75","DOIUrl":"https://doi.org/10.1109/IPDPS.2017.75","url":null,"abstract":"Nested fork-join programs scheduled using work stealing can automatically balance load and adapt to changes in the execution environment. In this paper, we design an approach to efficiently recover from faults encountered by these programs. Specifically, we focus on localized recovery of the task space in the presence of fail-stop failures. We present an approach to efficiently track, under work stealing, the relationships between the work executed by various threads. This information is used to identify and schedule the tasks to be re-executed without interfering with normal task execution. The algorithm precisely computes the work lost, incurs minimal re-execution overhead, and can recover from an arbitrary number of failures. Experimental evaluation demonstrates low overheads in the absence of failures, recovery overheads on the same order as the lost work, and much lower recovery costs than alternative strategies.","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":"132822982","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}
Jungwon Kim, Kittisak Sajjapongse, Seyong Lee, J. Vetter
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.
{"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}
Heterogeneous computing systems, e.g., those with accelerators than the host CPUs, offer the accelerated performance for a variety of workloads. However, most parallel programming models require platform dependent, time-consuming hand-tuning efforts for collectively using all the resources in a system to achieve efficient results. In this work, we explore the use of OpenMP parallel language extensions to empower users with the ability to design applications that automatically and simultaneously leverage CPUs and accelerators to further optimize use of available resources. We believe such automation will be key to ensuring codes adapt to increases in the number and diversity of accelerator resources for future computing systems. The proposed system combines language extensions to OpenMP, load-balancing algorithms and heuristics, and a runtime system for loop distribution across heterogeneous processing elements. We demonstrate the effectiveness of our automated approach to program on systems with multiple CPUs, GPUs, and MICs.
{"title":"HOMP: Automated Distribution of Parallel Loops and Data in Highly Parallel Accelerator-Based Systems","authors":"Yonghong Yan, Jiawen Liu, K. Cameron, M. Umar","doi":"10.1109/IPDPS.2017.99","DOIUrl":"https://doi.org/10.1109/IPDPS.2017.99","url":null,"abstract":"Heterogeneous computing systems, e.g., those with accelerators than the host CPUs, offer the accelerated performance for a variety of workloads. However, most parallel programming models require platform dependent, time-consuming hand-tuning efforts for collectively using all the resources in a system to achieve efficient results. In this work, we explore the use of OpenMP parallel language extensions to empower users with the ability to design applications that automatically and simultaneously leverage CPUs and accelerators to further optimize use of available resources. We believe such automation will be key to ensuring codes adapt to increases in the number and diversity of accelerator resources for future computing systems. The proposed system combines language extensions to OpenMP, load-balancing algorithms and heuristics, and a runtime system for loop distribution across heterogeneous processing elements. We demonstrate the effectiveness of our automated approach to program on systems with multiple CPUs, GPUs, and MICs.","PeriodicalId":209524,"journal":{"name":"2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)","volume":"16 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":"114423383","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}
MapReduce applications, which require access to a large number of computing nodes, are commonly deployed in heterogeneous environments. The performance discrepancy between individual nodes in a heterogeneous cluster present significant challenges to attain good performance in MapReduce jobs. MapReduce implementations designed and optimized for homogeneous environments perform poorly on heterogeneous clusters. We attribute suboptimal performance in heterogeneous clusters to significant load imbalance between map tasks. We identify two MapReduce designs that hinder load balancing: (1) static binding between mappers and their data makes it difficult to exploit data redundancy for load balancing; (2) uniform map sizes is not optimal for nodes with heterogeneous performance. To address these issues, we propose FlexMap, a user-transparent approach that dynamically provisions map tasks to match distinct machine capacity in heterogeneous environments. We implemented FlexMap in Hadoop-2.6.0. Experimental results show that it reduces job completion time by as much as 40% compared to stock Hadoop and 30% to SkewTune.
{"title":"Addressing Performance Heterogeneity in MapReduce Clusters with Elastic Tasks","authors":"Wei Chen, J. Rao, Xiaobo Zhou","doi":"10.1109/IPDPS.2017.28","DOIUrl":"https://doi.org/10.1109/IPDPS.2017.28","url":null,"abstract":"MapReduce applications, which require access to a large number of computing nodes, are commonly deployed in heterogeneous environments. The performance discrepancy between individual nodes in a heterogeneous cluster present significant challenges to attain good performance in MapReduce jobs. MapReduce implementations designed and optimized for homogeneous environments perform poorly on heterogeneous clusters. We attribute suboptimal performance in heterogeneous clusters to significant load imbalance between map tasks. We identify two MapReduce designs that hinder load balancing: (1) static binding between mappers and their data makes it difficult to exploit data redundancy for load balancing; (2) uniform map sizes is not optimal for nodes with heterogeneous performance. To address these issues, we propose FlexMap, a user-transparent approach that dynamically provisions map tasks to match distinct machine capacity in heterogeneous environments. We implemented FlexMap in Hadoop-2.6.0. Experimental results show that it reduces job completion time by as much as 40% compared to stock Hadoop and 30% to SkewTune.","PeriodicalId":209524,"journal":{"name":"2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)","volume":"29 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":"132444552","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}
Learn how hardware and software stacks enable not only quick prototyping, but also efficient large-scale production deployments. The tutorial will conclude with a discussion about hands-on deep learning training opportunities as well as free academic teaching materials and GPU cloud platforms for university faculty.
{"title":"NVIDIA Deep Learning Tutorial","authors":"J. Bernauer","doi":"10.1109/IPDPS.2017.7","DOIUrl":"https://doi.org/10.1109/IPDPS.2017.7","url":null,"abstract":"Learn how hardware and software stacks enable not only quick prototyping, but also efficient large-scale production deployments. The tutorial will conclude with a discussion about hands-on deep learning training opportunities as well as free academic teaching materials and GPU cloud platforms for university faculty.","PeriodicalId":209524,"journal":{"name":"2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)","volume":"59 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":"132925360","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}
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.
{"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}
We present methods for the effective application level reordering of non-blocking RDMA operations. We supplement out-of-order hardware delivery mechanisms with heuristics to account for the CPU side overhead of communication and for differences in network latency: a runtime scheduler takes into account message sizes, destination and concurrency and reorders operations to improve overall communication throughput. Results are validated on InfiniBand and Cray Aries networks, for SPMD and hybrid (SPMD+OpenMP) programming models. We show up to 5! potential speedup, with 30-50% more typical, for synthetic message patterns in microbenchmarks. We also obtain up to 33% improvement in the communication stages in application settings. While the design space is complex, the resulting scheduler is simple, both internally and at the application level interfaces. It also provides performance portability across networks and programming models. We believe these techniques can be easily retrofitted within any application or runtime framework that uses one-sided communication, e.g. using GASNet, MPI 3.0 RMA or low level APIs such as IBVerbs.
{"title":"Application Level Reordering of Remote Direct Memory Access Operations","authors":"W. Lavrijsen, Costin Iancu","doi":"10.1109/IPDPS.2017.98","DOIUrl":"https://doi.org/10.1109/IPDPS.2017.98","url":null,"abstract":"We present methods for the effective application level reordering of non-blocking RDMA operations. We supplement out-of-order hardware delivery mechanisms with heuristics to account for the CPU side overhead of communication and for differences in network latency: a runtime scheduler takes into account message sizes, destination and concurrency and reorders operations to improve overall communication throughput. Results are validated on InfiniBand and Cray Aries networks, for SPMD and hybrid (SPMD+OpenMP) programming models. We show up to 5! potential speedup, with 30-50% more typical, for synthetic message patterns in microbenchmarks. We also obtain up to 33% improvement in the communication stages in application settings. While the design space is complex, the resulting scheduler is simple, both internally and at the application level interfaces. It also provides performance portability across networks and programming models. We believe these techniques can be easily retrofitted within any application or runtime framework that uses one-sided communication, e.g. using GASNet, MPI 3.0 RMA or low level APIs such as IBVerbs.","PeriodicalId":209524,"journal":{"name":"2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)","volume":"203 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":"116174080","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}
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.
{"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}
Trends in the emerging digital economy are pushing the virtual representation of products and services. Creating these digital twins requires a combination of real time data ingestion, simulation of physical products under real world conditions, service delivery optimization and data analytics as well as ML/DL anomaly detection and decision making. Quantification of Uncertainty in the simulations will also be a compute and data intensive workflow that will drive the simulation improvement cycle. Future high-end computing systems designs need to comprehend these types of complex workflows and provide a flexible framework for optimizing the design and operations under dynamic load conditions for them.
{"title":"A Scalable System Architecture to Addressing the Next Generation of Predictive Simulation Workflows with Coupled Compute and Data Intensive Applications","authors":"M. Seager","doi":"10.1109/IPDPS.2017.129","DOIUrl":"https://doi.org/10.1109/IPDPS.2017.129","url":null,"abstract":"Trends in the emerging digital economy are pushing the virtual representation of products and services. Creating these digital twins requires a combination of real time data ingestion, simulation of physical products under real world conditions, service delivery optimization and data analytics as well as ML/DL anomaly detection and decision making. Quantification of Uncertainty in the simulations will also be a compute and data intensive workflow that will drive the simulation improvement cycle. Future high-end computing systems designs need to comprehend these types of complex workflows and provide a flexible framework for optimizing the design and operations under dynamic load conditions for them.","PeriodicalId":209524,"journal":{"name":"2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)","volume":"177 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":"124850288","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}
Teng Wang, A. Moody, Yue Zhu, K. Mohror, Kento Sato, T. Islam, Weikuan Yu
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.
{"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}
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