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20th International Middleware Conference Industrial Track (Middleware Industry)

作者： Park, SeongJae Lee, Yunjae Yeom, Heon Y. Seoul Natl Univ Seoul South Korea

ISBN: (纸本)9781450370417

Modern workloads tend to have huge working sets and low locality. Despite this trend, the capacity of DRAM has not been increased enough to accommodate such huge working sets. Therefore, memory management mechanisms optimized for such modern workloads are widely required today. For such optimizations, knowing the data access pattern of given workloads is essential. However, manually extracting such patterns from huge and complex workloads is exhaustive. Worse yet, existing memory access analysis tools incur unacceptably high overheads for unnecessarily detailed analysis results. To mitigate this situation, we introduce a tool that is designed for data access pattern tracing. Two core mechanisms in this tool, a region-based sampling and an adaptive region adjustment, allow users to limit the tracing overhead in a bounded range regardless of the size and complexity of target workloads, while preserving the quality of results. Our empirical evaluations that conducted with 20 realistic workloads show the high quality, low overhead, and a potential use case of this tool.

关键词： data access pattern memory management memory-intensive workloads profiler performance optimization

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Profiling Dynamic data access patterns with Bounded Overhead and Accuracy 4

Profiling Dynamic Data Access Patterns with Bounded Overhead...

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IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W)

作者： Park, SeongJae Lee, Yunjae Kim, Yoonhee Yeom, Heon Y. Seoul Natl Univ Dept Comp Sci & Engn Seoul South Korea Sookmyung Womens Univ Dept Comp Sci Seoul South Korea

ISBN: (纸本)9781728124063

One common characteristic of modern workloads such as cloud, big data, and machine learning is memory intensiveness. In detail, such workloads tend to have a huge working set and low locality. Especially, the size of working sets is rapidly growing so that cannot be fully accommodated by a DRAM based main memory. Worse yet, the cloud computing systems, which has been pervasive since few decades ago, are continuously reducing the size of DRAM per CPU and encouraging memory overcommitment. Consequently, efficient and effective out-of-core memory management is becoming more important. Though a number of memory management mechanisms for such situations have proposed, manual analysis and optimization are still required for optimal performance of each workload due to the wide variety of data access patterns. However, existing tools for memory access analysis are not appropriate to be used here because those are not designed for extraction of the dynamic data access pattern of modern workloads. When those tools are used for the purpose, those incur unacceptably high overheads for unnecessarily accurate analysis results. To mitigate this situation, we introduce a tool that is designed for the purpose. Basically, the tool employs a memory access tracking technique based on page table entry access bit, which incurs only minimal overhead. It also provides a technique for an effective tradeoff between profiling overheads and accuracy of the output by dynamically adjusting number of tracking regions. By adopting the technique, this tool can control the level of overheads and output accuracy in bounded range that user specified regardless of the size of target workloads. The overhead can be lowered even enough to be used for online target workloads while still providing useful quality of the extracted data access pattern. The main contributions of this paper are: 1) introduce of the data access patterns profiler tool designed for modern memory-intensive workloads, and 2) empirica

关键词： data access pattern memory-intensive workloads profiler performance optimization

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Optimizing Parallel I/O accesses through pattern-Directed and Layout-Aware Replication

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IEEE TRANSACTIONS ON COMPUTERS 2020年第2期69卷 212-225页

作者： He, Shuibing Yin, Yanlong Sun, Xian-He Zhang, Xuechen Li, Zongpeng Zhejiang Univ Coll Comp Sci & Technol Hangzhou 310058 Zhejiang Peoples R China Inst Artificial Intelligence Intelligent Comp Syst Res Ctr Zhejiang Lab Hangzhou 311100 Zhejiang Peoples R China IIT Dept Comp Sci Chicago IL 60616 USA Washington State Univ Sch Engn & Comp Sci Vancouver WA 98686 USA Wuhan Univ Sch Comp Sci Wuhan 430072 Hubei Peoples R China

As the performance gap between processors and storage devices keeps increasing, I/O performance becomes a critical bottleneck of modern high-performance computing systems. In this paper, we propose a pattern-directed and layout-aware data replication design, named PDLA, to improve the performance of parallel I/O systems. PDLA includes an HDD-based scheme H-PDLA and an SSD-based scheme S-PDLA. For applications with relatively low I/O concurrency, H-PDLA identifies access patterns of applications and makes a reorganized data replica for each access pattern on HDD-based servers with an optimized data layout. Moreover, to accommodate applications with high I/O concurrency, S-PDLA replicates critical access patterns that can bring performance benefits on SSD-based servers or on HDD-based and SSD-based servers. We have implemented the proposed replication scheme under MPICH2 library on top of OrangeFS file system. Experimental results show that H-PDLA can significantly improve the original parallel I/O system performance and demonstrate the advantages of S-PDLA over H-PDLA.

关键词： Parallel I/O I/O optimization data replication data reorganization data access pattern

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A Prefetch-Aware Memory System for data access patterns in Multimedia Applications 15

A Prefetch-Aware Memory System for Data Access Patterns in M...

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15th ACM International Conference on Computing Frontiers

作者： Alawneh, Tareq A. Elhossini, Ahmed Tech Univ Berlin Inst Comp Engn & Microelect Berlin Germany Al Azhar Univ Fac Engn Cairo Egypt

ISBN: (纸本)9781450357616

As the speed gap between CPU and external memory widens, memory latency has become the dominant performance bottleneck in modern applications. Closely connected are caches which play an important role in reducing the average memory latency. The way data is accessed strongly influences cache performance. Numerous multimedia algorithms operating on data such as images and videos, perform processing over rectangular regions of pixels. If this and other data access patterns are properly exploited, significant performance improvements can be achieved. This paper proposes a prefetch-aware memory system that exploits 2D, stride and sequential data access patterns in multimedia applications. It aims at reducing the average memory access latency, lowering the number of memory accesses and utilizing the bandwidth efficiently. Our results reveal significant average memory access time (AMAT) reduction of 21.2% when utilizing effectively the proposed approach compared to the baseline in the evaluated workloads.

关键词： DRAM row-buffer conflict bank-level parallelism prefetch-aware data access pattern two-dimensional sequential stride

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An access-pattern-Aware On-Chip Vector Memory System with Automatic Loading for SIMD Architectures

An Access-Pattern-Aware On-Chip Vector Memory System with Au...

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IEEE High Performance Extreme Computing Conference (HPEC)

作者： Geng, Tong Diken, Erkan Wang, Tianqi Jozwiak, Lech Herbordt, Martin Boston Univ Dept Elect & Comp Engn Boston MA 02215 USA Univ Sci & Technol China Dept Phys Hefei Anhui Peoples R China Eindhoven Univ Technol Dept Elect Engn Eindhoven Netherlands

ISBN: (纸本)9781538659892

Single-Instruction-Multiple-data (SIMD) architectures are widely used to accelerate applications involving data-Level Parallelism (DLP);the on-chip memory system facilitates the communication between Processing Elements (PE) and on-chip vector memory. It is observed that inefficiency of the onchip memory system is often a computational bottleneck. In this paper, we describe the design and implementation of an efficient vector data memory system. The proposed memory system consists of two novel parts: an access-pattern-aware memory controller and an automatic loading mechanism. The memory controller reduces the data reorganization overheads. The automatic loading mechanism loads data automatically according to the access patterns without load instructions. This eliminates overhead of fetching and decoding. The proposed design is implemented and synthesized with Cadence tools. Experimental results demonstrate that our design improves the performance of 8 application kernels by 44% and reduces the energy consumption by 26%, on average.

关键词： component-SIMD Vector data Memory data access pattern Automatic Loading

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When to Use Standards-Based APIs (Part 1)

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IEEE CLOUD COMPUTING 2015年第5期2卷 76-80页

作者： Sill, Alan Texas Tech Univ Ctr High Performance Comp Lubbock TX 79409 USA

This column completes a two-part exploration into features of application programming interfaces (APIs) that are useful in clouds. The discussion contrasts APIs with other types of interfaces and describes variations on protocols and calling methods, giving examples from physical hardware control to illustrate important features of cloud API design.

关键词： Application Program Interfaces Cloud Computing Standards Based API data access pattern Local User Driven Method Cloud Based Tools Application Programmer Interface API Design API Concept Cloud Computing Programming Application Programming Interfaces Context Modeling Technological Innovation Design Methodology Cloud Standards Programming data access API Design

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Adaptive data Placement For Staging-Based Coupled Scientific Workflows 15

Adaptive Data Placement For Staging-Based Coupled Scientific...

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International Conference for High Performance Computing, Networking, Storage and Analysis (SC)

作者： Sun, Qian Jin, Tong Romanus, Melissa Bui, Hoang Zhang, Fan Yu, Hongfeng Kolla, Hemanth Klasky, Scott Chen, Jacqueline Parashar, Manish Rutgers State Univ Rutgers Discovery Informat Inst Piscataway NJ 08854 USA Univ Nebraska Comp Sci & Engn Lincoln NE 68588 USA Sandia Natl Labs Livermore CA 94550 USA Oak Ridge Natl Lab Oak Ridge TN 37831 USA

ISBN: (纸本)9781450337236

data staging and in-situ/in-transit data processing are emerging as attractive approaches for supporting extreme scale scientific workflows. These approaches improve end-to-end performance by enabling runtime data sharing between coupled simulations and data analytics components of the workflow. However, the complex and dynamic data exchange patterns exhibited by the workflows coupled with the varied data access behaviors make efficient data placement within the staging area challenging. In this paper, we present an adaptive data placement approach to address these challenges. Our approach adapts data placement based on application-specific dynamic data access patterns, and applies access pattern-driven and location-aware mechanisms to reduce data access costs and to support efficient data sharing between the multiple workflow components. We experimentally demonstrate the effectiveness of our approach on Titan Cray XK7 using a real combustion-analyses workflow. The evaluation results demonstrate that our approach can effectively improve data access performance and overall efficiency of coupled scientific workflows.

关键词： Adaptive data placement data access pattern in-situ/in-transit coupled scientific workflows data staging

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Comparative Analysis of Adaptive File Replication Algorithms for Cloud data Storage 2

Comparative Analysis of Adaptive File Replication Algorithms...

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2nd International Conference on Future Internet of Things and Cloud (FiCloud)

作者： Myint, Julia Hunger, Axel Univ Duisburg Essen Dept Comp Engn Duisburg Germany Univ Comp Studies Yangon Myanmar

ISBN: (纸本)9781479943579

With the popularity of cloud computing technology, storage systems becomes an important role to distribute and store massive amount of data to data centers effectively. As grow as the explosive amount of data in cloud environment day after day, data popularity in cloud computing becomes unstable and unpredictable. Moreover, maintaining the static number of replicas in the system results highly storage cost for unpopular data and inefficient for most accessed data. To address these challenges, adaptive file replication algorithm called PopStore approach is proposed by using data access history and popularity thresholds. The performance of the replication algorithms is analyzed by using Yahoo Hadoop audit log to extract data access pattern. According to the experimental results, the PopStore strategy and LALW are able to adapt the access frequency and data popularity. In addition, PopStore can save more storage cost than LALW and network usage can be saved up to 35% more than LALW strategy.

关键词： cloud storage data popularity replication data access pattern

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Concentric layout, a new scientific data layout for matrix data-set in Hadoop file system

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INTERNATIONAL JOURNAL OF PARALLEL EMERGENT AND DISTRIBUTED SYSTEMS 2013年第5期28卷 407-433页

作者： Wang, Jun Cheng, Lu Wang, Lizhe Univ Cent Florida Dept Elect Engn & Comp Sci 4000 Cent Florida Blvd Orlando FL 32816 USA Chinese Acad Sci Ctr Earth Observat & Digital Earth Beijing 100094 Peoples R China

Due to the explosive growth in the size of scientific data-sets, data-intensive computing and analysing are an emerging trend in computational science. In these applications, data pre-processing is widely adopted because it can optimise the data layout or format beforehand to facilitate the future data access. On the other hand, current research shows an increasing popularity of MapReduce framework for large-scale data processing. However, the data access patterns which are generally applied to scientific data-set are not supported by current MapReduce framework directly. This gap motivates us to provide support for these scientific data access patterns in MapReduce framework. In our work, we study the data access patterns in matrix files and propose a new concentric data layout solution to facilitate matrix data access and analysis in MapReduce framework. Concentric data layout is a data layout which maintains the dimensional property in chunk level. Contrary to the continuous data layout adopted in the current Hadoop framework, concentric data layout stores the data from the same sub-matrix into one chunk. This layout can guarantee that the average performance of data access is optimal regardless of the various access patterns. The concentric data layout requires reorganising the data before it is being analysed or processed. Our experiments are launched on a real-world halo-finding application;the results indicate that the concentric data layout improves the overall performance by up to 38%.

关键词： data access pattern Hadoop distributed file system matrix file data layout

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pattern-Direct and Layout-Aware Replication Scheme for Parallel I/O Systems

Pattern-Direct and Layout-Aware Replication Scheme for Paral...

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IEEE 27th International Parallel and Distributed Processing Symposium (IPDPS)

作者： Yin, Yanlong Li, Jibing He, Jun Sun, Xian-He Thakur, Rajeev IIT Dept Comp Sci Chicago IL 60616 USA Argonne Natl Lab Math & Comp Sci Div 9700 S Cass Ave Argonne IL 60439 USA

ISBN: (纸本)9780769549712

The performance gap between computing power and the I/O system is ever increasing, and in the meantime more and more High Performance Computing (HPC) applications are becoming data intensive. This study describes an I/O data replication scheme, named pattern-Direct and Layout-Aware (PDLA) data replication scheme, to alleviate this performance gap. The basic idea of PDLA is replicating identified data access pattern, and saving these reorganized replications with optimized data layouts based on access cost analysis. A runtime system is designed and developed to integrate the PDLA replication scheme and existing parallel I/O system;a prototype of PDLA is implemented under the MPICH2 and PVFS2 environments. Experimental results show that PDLA is effective in improving data access performance of parallel I/O systems.

关键词： Parallel I/O I/O optimization data replication data reorganization data access pattern

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