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Conference on Image Processing - Algorithms and Systems

作者： Creutzburg, R FH Brandenburg Univ Appl Sci Dept Comp Sci & Media D-14737 Brandenburg Germany

ISBN: (纸本)0819444073

The aim of this paper that is organized in two parts is to introduce the concept of parallel access of data in regular but not orthogonal grids. Although the orthogonal grid and the corresponding sampling methods are well-known for many years and well established in science and technology, there is a certain interest in 2- and 3-dimensional imaging to study triagonal and hexagonal grids. In the 2-dimensional case these grids are generated by tessellation of the plane using triangles and hexagons, respectively. They form very regular patterns and they have very nice properties according to the number of neighbourhood pixels and distance values in electronic imaging. Moreover, it is known for a long time that the retina part of the human visual system can be modelled by a hexagonal packing structure of rods and cones. In this paper we study the connection and the influence of the necessary data structures, access patterns, and system architecture to model imaging algorithms with triagonal and hexagonal grids. In particular, we study the parallel access to straight lines and hexagonal "circles". We show a possible parallel memory architecture for the parallel conflict-free access to rows, straight lines and hexagonal "circles". The necessary fundamental notions are given in this first part.

关键词： parallel memory parallel data access hexagonal grid conflict-free access

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Achieving Load Balance for parallel data access on Distributed File Systems

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IEEE TRANSACTIONS ON COMPUTERS 2018年第3期67卷 388-402页

作者： Huang, Dan Han, Dezhi Wang, Jun Yin, Jiangling Chen, Xunchao Zhang, Xuhong Zhou, Jian Ye, Mao Univ Cent Florida Dept Elect Engn & Comp Sci Orlando FL 32816 USA Shanghai Maritime Univ Merchant Marine Coll Shanghai 201306 Peoples R China

The distributed file system, HDFS, is widely deployed as the bedrock for many parallel big data analysis. However, when running multiple parallel applications over the shared file system, the data requests from different processes/executors will unfortunately be served in a surprisingly imbalanced fashion on the distributed storage servers. These imbalanced access patterns among storage nodes are caused because a). unlike conventional parallel file system using striping policies to evenly distribute data among storage nodes, data-intensive file system such as HDFS store each data unit, referred to as chunk file, with several copies based on a relative random policy, which can result in an uneven data distribution among storage nodes;b). based on the data retrieval policy in HDFS, the more data a storage node contains, the higher probability the storage node could be selected to serve the data. Therefore, on the nodes serving multiple chunk files, the data requests from different processes/executors will compete for shared resources such as hard disk head and network bandwidth, resulting in a degraded I/O performance. In this paper, we first conduct a complete analysis on how remote and imbalanced read/write patterns occur and how they are affected by the size of the cluster. We then propose novel methods, referred to as Opass, to optimize parallel data reads, as well as to reduce the imbalance of parallel writes on distributed file systems. Our proposed methods can benefit parallel data-intensive analysis with various parallel data access strategies. Opass adopts new matching-based algorithms to match processes to data so as to compute the maximum degree of data locality and balanced data access. Furthermore, to reduce the imbalance of parallel writes, Opass employs a heatmap for monitoring the I/O statuses of storage nodes and performs HM-LRU policy to select a local optimal storage node for serving write requests. Experiments are conducted on PRObE's Marmot 128-no

关键词： parallel data access distributed file systems HDFS bipartite matching

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DARIC: A data Reuse-Friendly CGRA for parallel data access via Elastic FIFOs 60

DARIC: A Data Reuse-Friendly CGRA for Parallel Data Access v...

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60th ACM/IEEE Design Automation Conference (DAC)

作者： Liu, Dajiang Mou, Di Zhu, Rong Zhuang, Yan Shang, Jiaxing Zhong, Jiang Yin, Shouyi Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China Tsinghua Univ Sch Integrated Circuits Beijing 100084 Peoples R China

ISBN: (纸本)9798350323481

Coarse-Grained Reconfigurable Arrays (CGRAs) are a promising architecture for data-intensive applications. For parallel data accesses, uniform memory partitioning is usually introduced to CGRA for better pipelining performance. However, uniform memory partitioning not only suffers from a local minimum, but also introduces non-negligible overhead for banking function, which may greatly degrade the performance of CGRA. To this end, this paper introduces non-uniform memory partitioning and proposes a data-reuse-friendly CGRA (DARIC). With well elaborated configurable bank groups cooperated with register chains, elastic FIFOs can be achieved for non-uniform memory partitioning. Based on the resource graph of DARIC, a mapping algorithm supporting path sharing is proposed. Finally, the experimental results show that DARIC can achieve 2.35 x throughput and 2.59 x energy efficiency while having even less area and power overhead, as compared to the state-of-the-art.

关键词： CGRA data Reuse parallel data access FIFO

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Opass: Analysis and Optimization of parallel data access on Distributed File Systems 29

Opass: Analysis and Optimization of Parallel Data Access on ...

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29th IEEE International parallel and Distributed Processing Symposium (IPDPS)

作者： Yin, Jiangling Wang, Jun Zhou, Jian Lukasiewicz, Tyler Huang, Dan Zhang, Junyao Univ Cent Florida Dept Elect Engn & Comp Sci Orlando FL 32816 USA

ISBN: (纸本)9781479986484

In this paper, we study parallel data access on distributed file systems, e.g, the Hadoop file system. Our experiments show that parallel data read requests are often served data remotely and in an imbalanced fashion. This results in a serious disk access and data transfer contention on certain cluster/storage nodes. We conduct a complete analysis on how remote and imbalanced read patterns occur and how they are affected by the size of the cluster. We then propose a novel method to Optimize parallel data access on Distributed File Systems referred to as Opass. The goal of Opass is to reduce remote parallel data accesses and achieve a higher balance of data read requests between cluster nodes. To achieve this goal, we represent the data read requests that are issued by parallel applications to cluster nodes as a graph data structure where edges weights encode the demands of data locality and load capacity. Then we propose new matching-based algorithms to match processes to data based on the configurations of the graph data structure so as to compute the maximum degree of data locality and balanced access. Our proposed method can benefit parallel data-intensive analysis with various parallel data access strategies. Experiments are conducted on PRObEs Marmot 128-node cluster testbed and the results from both benchmark and well-known parallel applications show the performance benefits and scalability of Opass.

关键词： parallel data access Distributed File Systems Bipartite Matching

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Multibank Memory Optimization for parallel data access in Multiple data Arrays 35

Multibank Memory Optimization for Parallel Data Access in Mu...

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35th IEEE/ACM International Conference on Computer-Aided Design (ICCAD)

作者： Yin, Shouyi Xie, Zhicong Meng, Chenyue Liu, Leibo Wei, Shaojun Tsinghua Univ Inst Microelect Beijing 100084 Peoples R China

ISBN: (纸本)9781450344661

To realize high throughput out of a relatively low bandwidth, memory partitioning algorithms have been proposed to separate data arrays into multiple memory banks, from which multiple data can be accessed in parallel. However, previous partitioning schemes only considered the case of single-pattern and single-array. In this paper, we propose an efficient two-step memory partitioning strategy for multi-pattern access in multiple multi-dimensional arrays. First, a fast, low complexity and low difference-baseddata splitting algorithm provides a multi-bank solution for multiple patterns access. Then an area-efficiency bank merging algorithm reduce the area overhead caused by partitioning. Experimental results show that our memory splitting algorithm saves up to 83.0% in searching time finding a multi-bank solution, compared to the state-of-the-art approach and the storage overhead can be reduced by 34.5%. Meanwhile the area overheads are saved up to 18.86% and the whole partition time are saved up to 45.6% through our entire algorithm.

关键词： Memory Partitioning Bank Merging parallel data access

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Efficient Memory Partitioning for parallel data access in Multidimensional Arrays 15

Efficient Memory Partitioning for Parallel Data Access in Mu...

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52nd ACM/EDAC/IEEE Design Automation Conference (DAC)

作者： Meng, Chenyue Yin, Shouyi Ouyang, Peng Liu, Leibo Wei, Shaojun Tsinghua Univ Inst Microelect TNList Beijing 100084 Peoples R China

ISBN: (纸本)9781450335201

Memory bandwidth bottlenecks severely restrict parallel access of data from memory arrays. To increase bandwidth, memory partitioning algorithms have been proposed to access multiple memory banks simultaneously. However, previous partitioning schemes propose complex partitioning algorithms, which leads to non-optimal memory bank space utilization and unnecessary storage overhead. In this paper, we develop an efficient memory partitioning strategy with low time complexity and low storage overhead for data access in multidimensional arrays. Experimental results show that our memory partitioning algorithm saves up to 93.7% in the amount of arithmetic operations, 96.9% in execution time and 31.1% in storage overhead, compared to the state-of-the-art approach.

关键词： memory partitioning parallel data access storage overhead

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Memory Partitioning for parallel Multipattern data access in Multiple data Arrays

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 2018年第2期37卷 431-444页

作者： Yin, Shouyi Xie, Zhicong Meng, Chenyue Ouyang, Peng Liu, Leibo Wei, Shaojun Tsinghua Univ Inst Microelect Beijing 100084 Peoples R China

Memory bandwidth bottlenecks severely restrict parallel access of data elements from data arrays. To realize high throughput out of a relatively low bandwidth, memory partitioning algorithms have been proposed to separate data arrays into multiple memory banks, from which multiple data can be accessed in parallel. However, previous partitioning schemes only considered the case of single-pattern and single-array. In the case of multipattern and multiarray, the previous partitioning schemes will use too much time to find a partition solution and cause excessively high storage overhead. In this paper, we propose an efficient two-step memory partitioning strategy for multipattern data access in multiple arrays. First, a fast, low complexity and low overhead difference-based data splitting algorithm provides a multibank solution for multiple patterns access. Then an area-efficient bank merging algorithm merges those partitioned banks from different arrays which satisfy conflict-free requirement in order to reduce the area overhead caused by partitioning. Experimental results show that our data splitting algorithm saves up to 83.0% in searching time and reduces 39.4% storage overhead compared to the state-of-the-art approaches. With the further optimization of area-efficient bank merging, the memory area overhead are saved up to 18.9% and the total partitioning time are saved up to 45.6%.

关键词： Bank merging memory partitioning parallel data access

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A 12-nm High-Density Energy-Efficient 1-Mb 2R2W Scratchpad With Local Blocks for Neural Network Applications

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IEEE JOURNAL OF SOLID-STATE CIRCUITS 2025年第2期60卷 707-718页

作者： Zhang, Hao He, Weifeng Dehaene, Wim Katholieke Univ Leuven KU Leuven Dept Elect Engn ESAT MICAS B-3001 Leuven Belgium Shanghai Jiao Tong Univ Dept Micronano Elect Shanghai 200240 Peoples R China

With the increasing scale and complexity of neural networks, high-bandwidth multi-port scratchpads become critical to improve system performance and energy efficiency. However, conventional multi-port scratchpad features a low storage density and a small memory capacity, resulting in limited performance improvements. In this article, we propose a high-density and energy-efficient scratchpad with two read and two write (2R2W) ports based on customized local blocks and a hierarchical accessing approach. Specifically, different global ports can access different blocks at the same time, where local read or write operations are performed with single-port bitcells. For an optimal storage density, push-rule high-density bitcells are used with metal pitch-matched local peripheries inside the block. Besides, hierarchical wordline decoding and fishbone-like vertical signal routing are presented to mitigate the density loss due to massive global signals. To improve the energy efficiency, dual-swing signaling, hierarchical timing scheme, and cascade sensing scheme are developed with negligible area overhead. A 1-Mb 2R2W scratchpad is prototyped in a 12-nm FinFET CMOS technology, achieving a storage density of 3.73 bit/ mu m(2) , which is superior to the state-of-the-art (SotA). The measurements demonstrate functionality from 180 MHz at 0.45 V to 950 MHz at 0.8 V, and a peak energy efficiency of 44.1 Tbps/W, which is 72.3% higher than SotA. Thus, with area-aware design strategies and energy-efficient circuit techniques, the proposed multi-port scratchpad is beneficial for neural network applications.

关键词： Energy-efficient memory high-density memory local block organization multi-port memory neural network processor parallel data access scratchpad

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Modeling distributed data representation and its effect on parallel data accesses

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JOURNAL OF parallel AND DISTRIBUTED COMPUTING 2005年第10期65卷 1281-1289页

作者： Jin, DJ Ziavras, SG New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA

PC clusters have emerged as viable alternatives for high-performance, low-cost computing. In such an environment, sharing data among processes is essential. accessing the shared data, however, may often stall parallel executing threads. We propose a novel data representation scheme where an application data entity can be incarnated into a set of objects that are distributed in the cluster. The runtime support system manages the incarnated objects and data access is possible only via an appropriate interface. This distributed data representation facilitates parallel accesses for updates. Thus, tasks are subject to few limitations and application programs can harness high degrees of parallelism. Our PC cluster experiments prove the effectiveness of our approach. (c) 2005 Elsevier Inc. All rights reserved.

关键词： data encapsulation distributed data parallel data access PC cluster super-programming model

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An Efficient Memory Partitioning Approach for Multi-Pattern data access in STT-RAM 52

An Efficient Memory Partitioning Approach for Multi-Pattern ...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Liu, Binbin Yang, Fan Zhou, Dian Zeng, Xuan Fudan Univ Sch Microelect State Key Lab ASIC & Syst Shanghai Peoples R China Univ Texas Dallas Dept Elect Engn Richardson TX 75083 USA

ISBN: (纸本)9781728133201

This paper presents an efficient memory partitioning algorithm for multi-pattern data access in Spin-transfer torque random access memory (STT-RAM). The algorithm generates check patterns to solve read disturbance problem while ensuring multi-pattern data access in parallel. The implementation results imply that our proposed approach highly outperforms state-of-the-art algorithm in terms of bank number, searching time, performance and storage overhead.

关键词： memory partitioning multi-pattern STT-RAM parallel data access

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