9 results on '"Sun, Xian-He"'
Search Results
2. A Cost-Effective Distribution-Aware Data Replication Scheme for Parallel I/O Systems.
- Author
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He, Shuibing and Sun, Xian-He
- Subjects
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DATA replication , *COST effectiveness , *PARALLEL computers , *COMPUTER input-output equipment , *CLIENT/SERVER computing , *DATA modeling - Abstract
As data volumes of high-performance computing applications continuously increase, low I/O performance becomes a fatal bottleneck of these data-intensive applications. Data replication is a promising approach to improve parallel I/O performance. However, most existing strategies are designed based on the assumption that contiguous requests are being served more efficiently than non-contiguous requests, which is not necessarily true in a parallel I/O system. The reason is that the multiple-server data distribution makes the favorable accesses between contiguous requests and non-contiguous ones indeterminate. In this study, we propose CEDA, a cost-effective distribution-aware data replication scheme to better support parallel I/O systems. As logical file access information is inefficient to make replication decisions in a parallel environment, CEDA considers physical data accesses on servers in both data selection and data placement during a parallel replication process. Specifically, CEDA first proposes a distribution-aware cost model to evaluate the file request time with a given data layout, and then it carries out cost-effective data replication based on replication benefit analysis. We have implemented CEDA as a part of the MPI I/O library in light of high portability on top of the OrangeFS file system. By replaying representative benchmarks and a real application, we collected comprehensive experimental results on both HDD- and SSD-based servers and conclude that CEDA can significantly improve parallel I/O system performance. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
3. Cost-Aware Region-Level Data Placement in Multi-Tiered Parallel I/O Systems.
- Author
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He, Shuibing, Wang, Yang, Li, Zheng, Sun, Xian-He, and Xu, Chenzhong
- Subjects
PARALLEL computers ,HIGH performance computing ,HARD disks ,COMPUTER input-output equipment ,CLIENT/SERVER computing - Abstract
Multi-tiered Parallel I/O systems that combine traditional HDDs with emerging SSDs mitigate the cost burden of SSDs while benefiting from their superior I/O performance. While a multi-tiered parallel I/O system is promising for data-intensive applications in high-performance (HPC) domains, placing data on each tier of the system to achieve high I/O performance remains a challenge. In this paper, we propose a cost-aware region-level (CARL) data placement scheme in multi-tiered parallel I/O systems. CARL divides a large file into several small regions, and then places regions on different types of servers based on region access costs. CARL includes a static policy S-CARL and a dynamic policy D-CARL. For applications whose I/O access patterns are completely known, S-CARL calculates the region costs within the entire workload duration, and uses a static data placement scheme to selectively place regions on the proper servers. To adapt to applications whose access patterns are unknown in advance, D-CARL uses a dynamic data placement scheme which migrates data among different servers within each time window. We have implemented CARL under MPI-IO library and OrangeFS parallel file system environment. Our evaluation with representative benchmarks and an application shows that CARL is both feasible and able to improve I/O performance significantly. [ABSTRACT FROM PUBLISHER]
- Published
- 2017
- Full Text
- View/download PDF
4. HARL: Optimizing Parallel File Systems with Heterogeneity-Aware Region-Level Data Layout.
- Author
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He, Shuibing, Wang, Yang, Sun, Xian-He, and Xu, Chengzhong
- Subjects
PARALLEL file systems (Computer science) ,SOLID state drives ,DATA distribution ,INFORMATION resources management ,ACCESS to information - Abstract
Parallel file system (PFS) is commonly used in high-end computing systems. With the emergence of solid state drives (SSDs), hybrid PFS, which consists of both HDD and SSD servers, provides a practical I/O system solution for data-intensive applications. However, most existing data layout schemes are inefficient for hybrid PFS due to their unawareness of server heterogeneities and workload changes in different parts of a file. In this study, we propose a heterogeneity-aware region-level data layout scheme, HARL, to improve the data distribution of a hybrid PFS. HARL first divides a file into fine-grained, varying sized regions according to the workload features of an application, then determines appropriate file stripe sizes on servers for each region based on the performance of heterogeneous servers. Furthermore, to further improve the performance of a hybrid PFS, we propose a dynamic region-level layout scheme, HARL-D, which creates multiple replicas for each region and redirects file requests to the proper replicas with the lowest access costs at the runtime. Experimental results of representative benchmarks and a real application show that HARL can greatly improve I/O system performance, and demonstrate the advantages of HARL-D over HARL. [ABSTRACT FROM AUTHOR]
- Published
- 2017
- Full Text
- View/download PDF
5. Enhancing hybrid parallel file system through performance and space-aware data layout.
- Author
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He, Shuibing, Liu, Yan, Wang, Yang, Sun, Xian-He, and Huang, Chuanhe
- Subjects
DATA analysis ,STEREOTYPE content model ,SOCIAL groups ,PARALLEL file systems (Computer science) ,DATA disk drives - Abstract
Hybrid parallel file systems (PFSs), which consist of solid-state drive servers (SServer) and hard disk drive servers (HServer), have recently attracted growing attention. Compared to a traditional HServer, an SServer consistently provides improved storage performance but lacks storage space. However, most current data layout schemes do not consider the differences in performance and space between heterogeneous servers and may significantly degrade the performance of the hybrid PFSs. In this article, we propose performance and space-aware (PSA) scheme, a novel data layout scheme, which maximizes the hybrid PFSs’ performance by applying adaptive varied-size file stripes. PSA dispatches data on heterogeneous file servers not only based on storage performance but also storage space. We have implemented PSA within OrangeFS, a popular PFS in the high-performance computing domain. Our extensive experiments with representative benchmarks, including IOR, HPIO, MPI-TILE-IO, and BTIO, show that PSA provides superior I/O throughput than the default and performance-aware file data layout schemes. [ABSTRACT FROM AUTHOR]
- Published
- 2016
- Full Text
- View/download PDF
6. Boosting Parallel File System Performance via Heterogeneity-Aware Selective Data Layout.
- Author
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He, Shuibing, Wang, Yang, and Sun, Xian-He
- Subjects
PARALLEL file systems (Computer science) ,HARD disks ,HETEROGENEITY ,DATA distribution ,INDUSTRIAL efficiency - Abstract
Hybrid parallel file systems (PFS) that combine HDD servers with SSD servers provide a promising solution for data intensive applications. The efficiency of a hybrid PFS relies on the data layout schemes. However, most current layout strategies are designed for homogeneous servers, which neither address the heterogeneity of servers nor the varying access patterns of applications. In this paper, we propose HAS, a novel heterogeneity-aware selective data layout scheme for hybrid PFSs. HAS alleviates inter-server load imbalance through skewing data distribution on heterogeneous servers based on their storage performance. Furthermore, to obtain the optimal performance for a specific access pattern, HAS selects one static data layout policy with lowest access cost from three typical layout candidates as the final file data layout method. To adapt to the mixed access patterns within an application, HAS uses a dynamic data layout scheme, which stores file with multiple copies, each using a different data layout policy, and then selects the copy with the lowest access cost to serve file requests. We have implemented HAS within MPICH2 and OrangeFS. Experimental results show that HAS can significantly increase the I/O throughput of hybrid PFSs, compared to existing data layout optimization methods. [ABSTRACT FROM PUBLISHER]
- Published
- 2016
- Full Text
- View/download PDF
7. Checkpointing Orchestration: Toward a Scalable HPC Fault-Tolerant Environment.
- Author
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Jin, Hui, Ke, Tao, Chen, Yong, and Sun, Xian-He
- Abstract
Check pointing is widely used in technical computing. However, the overhead of check pointing is a subject of increasing in concern in recent years, especially for large-scale parallel computer systems. In these systems, check pointing generates a huge number of concurrent I/O writes. The burst of writes plus the worsening I/O-wall problem often leads to network and I/O congestion, and makes the overall system performance painfully slow. Recognizing contention as a dominant performance factor, in this paper we propose a systematic approach named check pointing orchestration to reduce write contention, which combines the marshaling of concurrent checkpoint requests and the adopting of vertical data access in coordination. A prototype of the proposed check pointing orchestration approach has been implemented at the system-level under Open MPI over the PVFS2 file system. Extensive experiments based on NPB benchmarks have been conducted to verify the design and implementation. Experimental results show that check pointing orchestration reduced the check pointing cost at a degree of more than 30%. Check pointing cost was halved for 4 out of 5 the C class NPB benchmarks. [ABSTRACT FROM PUBLISHER]
- Published
- 2012
- Full Text
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8. A Server-Level Adaptive Data Layout Strategy for Parallel File Systems.
- Author
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Song, Huaiming, Jin, Hui, He, Jun, Sun, Xian-He, and Thakur, Rajeev
- Abstract
Parallel file systems are widely used for providing a high degree of I/O parallelism to mask the gap between I/O and memory speed. However, peak I/O performance is rarely attained due to complex data access patterns of applications. Based on the observation that the I/O performance of small requests is often limited by the request service rate, and the performance of large requests is limited by I/O bandwidth, we take into consideration both factors and propose a server-level adaptive data layout strategy. The proposed strategy adopts different stripe sizes for different file servers according to the data access characteristics on each individual server. We let the file servers that can fully utilize bandwidth hold more data, and the file servers that are limited with request service rate hold less data. As a result, heavy-load servers can offload some data accesses to light-load servers for potential improvement of I/O performance. We present a method to measure access cost for each data block and then utilize an equal-depth histogram approach to distributed data blocks across multiple servers adaptively, so as to balance data accesses on all file servers. Analytical and experimental results demonstrate that the proposed server-level adaptive layout strategy can improve I/O performance by as much as 80.3% and is more appropriate for applications with complex data access patterns. [ABSTRACT FROM PUBLISHER]
- Published
- 2012
- Full Text
- View/download PDF
9. Discovering Structure in Unstructured I/O.
- Author
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He, Jun, Bent, John, Torres, Aaron, Grider, Gary, Gibson, Garth, Maltzahn, Carlos, and Sun, Xian-He
- Abstract
Checkpointing is the predominant storage driver in today's petascale supercomputers and is expected to remain as such in tomorrow's exascale supercomputers. Users typically prefer to checkpoint into a shared file yet parallel file systems often perform poorly for shared file writing. A powerful technique to address this problem is to transparently transform shared file writing into many exclusively written as is done in ADIOS and PLFS. Unfortunately, the metadata to reconstruct the fragments into the original file grows with the number of writers. As such, the current approach cannot scale to exaflop supercomputers due to the large overhead of creating and reassembling the metadata. In this paper, we develop and evaluate algorithms by which patterns in the PLFS metadata can be discovered and then used to replace the current metadata. Our evaluation shows that these patterns reduce the size of the metadata by several orders of magnitude, increase the performance of writes by up to 40 percent, and the performance of reads by up to 480 percent. This contribution therefore can allow current checkpointing models to survive the transition from peta- to exascale. [ABSTRACT FROM PUBLISHER]
- Published
- 2012
- Full Text
- View/download PDF
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