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52nd ACM/EDAC/IEEE Design Automation Conference, DAC 2015

作者： Wang, Ying Han, Yinhe Wang, Cheng Li, Huawei Li, Xiaowei State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781450335201

Refresh operations consume substantial energy and bandwidth in high-density DRAM memory. To cope with this issue, Fine-Grained Refresh (FGR) is recently proposed to eliminate unnecessary refresh operations caused by minor weak cells. Even JEDEC's DDR4 DRAM specification announces the support of FGR to make DRAM refresh more scalable. Unfortunately, we observe that the effectiveness of FGR is greatly confined by the procedure of refresh-oblivious device integration because all memory devices within a module have to be controlled and refreshed in a lockstep way after the step of assembly. In this work, we firstly propose to intelligently integrate the "compatible" devices through a pre-assembly testing and retention-aware matching method. Second, we reuse the reconfiguration structure from yield-oriented remapping mechanism in memory chips and propose Microfix to create a balanced distribution of retention time in memory banks through fine-grained row-address tuning. With this optimization architecture, RADAR, we can eliminate the refresh overhead of produced memory modules by 28% on average. © 2015 ACM.

关键词： Dynamic random access storage

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An efficient parallel mechanism for highly-debuggable multicore simulator

An efficient parallel mechanism for highly-debuggable multic...

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10th International Symposium on Advanced Parallel Processing Technologies, APPT 2013

作者： Ye, Xiaochun Fan, Dongrui Wang, Da Song, Fenglong Zhang, Hao Tang, Zhimin State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783642452925

Fast multicore simulators are extremely useful in evaluating design alternatives and enabling early software development. Among the state-of-the-art multicore simulators, Simics is a very popular used one both in academia and industry. It has a powerful debugging system, and also provides an accelerator to support multithreaded or distributed simulation. However, this kind of parallel mechanism mainly aims at speed up distributed systems. It is not suitable for the shared-memory multicore systems which are much more commonly used. In this paper, we propose a novel parallel mechanism to improve the simulation speed of shared-memory multicore systems. More importantly, our approach is compatible with other optimizations and exist debugging systems used in Simics. Experimental results show that our parallel approach achieves an average speedup of 9.6x (up to 12.2x) when running SPLASH-2 kernel on a 16-core host machine. © 2013 Springer-Verlag.

关键词： Simulators

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Constrained maximum weighted bipartite matching:a novel approach to radio broadcast scheduling

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Science China(Information Sciences) 2019年第7期62卷 156-169页

作者： Shaojiang WANG Tianyong WU Yuan YAO Dongbo BU Shaowei CAI State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of Sciences School of Computer Science and Technology University of Chinese Academy of Sciences Beijing Key Lab of Human-Computer Interaction Institute of SoftwareChinese Academy of Sciences Institute of Computing Technology Chinese Academy of Sciences

Given a set of radio broadcast programs, the radio broadcast scheduling problem is to allocate a set of devices to transmit the programs to achieve the optimal sound quality. In this article, we propose a complete algorithm to solve the problem, which is based on a branch-and-bound(BnB) algorithm. We formulate the problem with a new model, called constrained maximum weighted bipartite matching(CMBM),i.e., the maximum matching problem on a weighted bipartite graph with constraints. For the reduced matching problem, we propose a novel BnB algorithm by introducing three new strategies, including the highest quality first, the least conflict first and the more edge first. We also establish an upper bound estimating function for pruning the search space of the algorithm. The experimental results show that our new algorithm can quickly find the optimal solution for the radio broadcast scheduling problem at small scales, and has higher scalability for the problems at large scales than the existing complete algorithm.

关键词： radio broadcast scheduling branch-and-bound algorithm constrained maximum weighted bipartite matching Kuhn-Munkres algorithm strategy combinations

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Shared work list: Hacking amorphous data parallelism in UPC

Shared work list: Hacking amorphous data parallelism in UPC

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2012 International Workshop on Programming Models and Applications for Multicores and Manycores, PMAM 2012

作者： Xu, Shixiong Chen, Li State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781450312110

"Irregular" algorithms using data structures like sparse graphs, trees and sets prevail in the most emerging problems domains such as social network analysis, machine learning, data mining and computational science. The irregularity of underlying data structures leads to unstructured parallelism in these algorithms, consequently making it pretty hard for users to write efficient parallel implementations on distributed memory systems. Unified Parallel C language provides convenience of a global address space with the locality control needed for high performance and scalability. However, the Single Program Multiple Data execution model with a statically fixed set of executing threads makes UPC does not support applications with unstructured parallelism. In this paper, we first put forward Shared Work List to UPC and advocate a programming paradigm for writing applications with amorphous data parallelism on distributed memory systems. We also introduce user-assisted speculative execution based on Active Message model to support speculative execution on distributed memory systems. Efficient mechanism of work dispatching and related optimizations are presented as well. We preliminarily choose Breadth-first Search as a case study to demonstrate the feasibility, pro-grammability and performance benefits out of Shared Work List. © 2012 ACM.

关键词： Data mining

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An optimized large-scale hybrid DGEMM design for CPUs and ATI GPUs 12

An optimized large-scale hybrid DGEMM design for CPUs and AT...

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26th ACM International Conference on Supercomputing, ICS'12

作者： Li, Jiajia Li, Xingjian Tan, Guangming Chen, Mingyu Sun, Ninghui State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781450313162

In heterogeneous systems that include CPUs and GPUs, the data transfers between these components play a critical role in determining the performance of applications. Software pipelining is a common approach to mitigate the overheads of those transfers. In this paper we investigate advanced software-pipelining optimizations for the double-precision general matrix multiplication (DGEMM) algorithm running on a heterogeneous system that includes ATI GPUs. Our approach decomposes the DGEMM workload to a finer detail and hides the latency of CPU-GPU data transfers to a higher degree than previous approaches in literature. We implement our approach in a five-stage software pipelined DGEMM and analyze its performance on a platform including x86 multi-core CPUs and an ATI Radeon™ HD5970 GPU that has two Cypress GPU chips on board. Our implementation delivers 758 GFLOPS (82% floating-point efficiency) when it uses only the GPU, and 844 GFLOPS (80% efficiency) when it distributes the workload on both CPU and GPU. We analyze the performance of our optimized DGEMM as the number of GPU chips employed grows from one to two, and the results show that resource contention on the PCIe bus and on the host memory are limiting factors. Copyright 2012 ACM.

关键词： Graphics processing unit

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Optimizing Multi-Dimensional Packet Classification for Multi-Core Systems

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Journal of computer Science & technology 2018年第5期33卷 1056-1071页

作者： Tong Shen Da-Fang Zhang Gao-Gang Xie Xin-Yi Zhang College of Computer Science and Electronic Engineering Hunan University Changsha 410082 China Network Technology Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China

Packet classification has been studied for decades; it classifies packets into specific flows based on a given rule set. As software-defined network was proposed, a recent trend of packet classification is to scale the five-tuple model to multi-tuple. In general, packet classification on multiple fields is a complex problem. Although most existing software-based algorithms have been proved extraordinary in practice, they are only suitable for the classic five-tuple model and difficult to be scaled up. Meanwhile, hardware-specific solutions are inflexible and expensive, and some of them are power consuming. In this paper, we propose a universal multi-dimensional packet classification approach for multi-core systems. In our approach, novel data structures and four decomposition-based algorithms are designed to optimize the classification and updating of rules. For multi-field rules, a rule set is cut into several parts according to the number of fields. Each part works independently. In this way, the fields are searched in parallel and all the partial results are merged together at last. To demonstrate the feasibility of our approach, we implement a prototype and evaluate its throughput and latency. Experimental results show that our approach achieves a 40% higher throughput than that of other decomposed-based algorithms and a 43% lower latency of rule incremental update than that of the other algorithms on average. Furthermore, our approach saves 39% memory consumption on average and has a good scalability.

关键词： multi-dimensional multi-core packet classification

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scCompass: An Integrated Multi-Species scRNA-seq Database for AI-Ready

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Advanced Science 2025年 e2500870页

作者： Wang, Pengfei Liu, Wenhao Wang, Jiajia Liu, Yana Li, Pengjiang Xu, Ping Cui, Wentao Zhang, Ran Long, Qingqing Hu, Zhilong Fang, Chen Dong, Jingxi Zhang, Chunyang Chen, Yan Wang, Chengrui Liu, Guole Xie, Hanyu Zhang, Yiyang Xiao, Meng Chen, Shubai Jiang, Haiping Chen, Yiqiang Yang, Ge Zhang, Shihua Meng, Zhen Wang, Xuezhi Feng, Guihai Li, Xin Zhou, Yuanchun Computer Network Information Center Chinese Academy of Sciences Beijing100083 China State Key Laboratory of Stem Cell and Reproductive Biology Institute of Zoology Chinese Academy of Sciences Beijing100101 China Institute for Stem Cell and Regenerative Medicine Chinese Academy of Sciences Beijing100101 China Beijing Institute for Stem Cell and Regenerative Medicine Beijing100101 China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China CEMS NCMIS HCMS MDIS RCSDS Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100864 China College of Life Science Northeast Agricultural University Harbin150030 China

Emerging single-cell sequencing technology has generated large amounts of data, allowing analysis of cellular dynamics and gene regulation at the single-cell resolution. Advances in artificial intelligence enhance life sciences research by delivering critical insights and optimizing data analysis processes. However, inconsistent data processing quality and standards remain to be a major challenge. Here scCompass is proposed, which provides a comprehensive resource designed to build large-scale, multi-species, and model-friendly single-cell data collection. By applying standardized data pre-processing, scCompass integrates and curates transcriptomic data from nearly 105 million single cells across 13 species. Using this extensive dataset, it is able to identify stable expression genes (SEGs) and organ-specific expression genes (OSGs) in humans and mice. Different scalable datasets are provided that can be easily adapted for AI model training and the pretrained checkpoints with state-of-the-art single-cell foundation models. In summary, scCompass is highly efficient and scalable database for AI-ready, which combined with user-friendly data sharing, visualization, and online analysis, greatly simplifies data access and exploitation for researchers in single-cell biology (http://***/kun). © 2025 The Author(s). Advanced Science published by Wiley-VCH GmbH.

关键词： Data Sharing

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dCompaction： Speeding up Compaction of the LSM-Tree via Delayed Compaction

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Journal of computer Science & technology 2017年第1期32卷 41-54页

作者： Feng-Feng Pan Yin-Liang Yue Jin Xiong State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China Institute of Information Engineering Chinese Academy of Sciences Beijing 100093 China

key-value （KV） stores have become a backbone of large-scale applications in today＇s data centers. Write- optimized data structures like the Log-Structured Merge-tree （LSM-tree） and their variants are widely used in KV storage systems like BigTable and RocksDB. Conventional LSM-tree organizes KV items into multiple, successively larger components, and uses compaction to push KV items from one smaller component to another adjacent larger component until the KV items reach the largest component. Unfortunately, current compaction scheme incurs significant write amplification due to repeated KV item reads and writes, and then results in poor throughput. We propose a new compaction scheme, delayed compaction （dCompaction） that decreases write amplification, dCompaction postpones some compactions and gathers them into the following compaction. In this way, it avoids KV item reads and writes during compaction, and consequently improves the throughput of LSM-tree based KV stores. We implement dCompaction on RocksDB, and conduct extensive experiments. Validation using YCSB framework shows that compared with RocksDB, dCompaction has about 40% write performance improvements and also comparable read performance.

关键词： key-value store Log-Structured Merge-tree （LSM-tree） write amplification delayed compaction

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Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects

arXiv

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arXiv 2025年

作者： Ma, Chenxiang Chen, Xinyi Li, Yanchen Yang, Qu Wu, Yujie Li, Guoqi Pan, Gang Tang, Huajin Tan, Kay Chen Wu, Jibin Department of Data Science and Artificial Intelligence The Hong Kong Polytechnic University Hong Kong Department of Computing The Hong Kong Polytechnic University Hong Kong Department of Electrical and Computer Engineering National University of Singapore 119077 Singapore Institute of Automation Chinese Academy of Sciences Beijing100045 China State Key Laboratory of Brain-Machine Intelligence College of Computer Science and Technology MOE Frontier Science Center for Brain Science and Brain-Machine Integration Zhejiang University Hangzhou310027 China

Temporal processing is fundamental for both biological and artificial intelligence systems, as it enables the comprehension of dynamic environments and facilitates timely responses. Spiking Neural Networks (SNNs) excel in handling such data with high efficiency, owing to their rich neuronal dynamics and sparse activity patterns. Given the recent surge in the development of SNNs, there is an urgent need for a comprehensive evaluation of their temporal processing capabilities. In this paper, we first conduct an in-depth assessment of commonly used neuromorphic benchmarks, revealing critical limitations in their ability to evaluate the temporal processing capabilities of SNNs. To bridge this gap, we further introduce a benchmark suite consisting of three temporal processing tasks characterized by rich temporal dynamics across multiple timescales. Utilizing this benchmark suite, we perform a thorough evaluation of recently introduced SNN approaches to elucidate the current status of SNNs in temporal processing. Our findings indicate significant advancements in recently developed spiking neuron models and neural architectures regarding their temporal processing capabilities, while also highlighting a performance gap in handling long-range dependencies when compared to state-of-the-art non-spiking models. Finally, we discuss the key challenges and outline potential avenues for future research. Copyright © 2025, The Authors. All rights reserved.

关键词： Spatio-temporal data

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Accelerating Data Transfer in Dataflow architectures Through a Look-Ahead Acknowledgment Mechanism

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Journal of computer Science & technology 2022年第4期37卷 942-959页

作者： Yu-Jing Feng De-Jian Li Xu Tan Xiao-Chun Ye Dong-Rui Fan Wen-Ming Li Da Wang Hao Zhang Zhi-Min Tang State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China Beijing Smartchip Microelectronics Technology Company Limited Beijing 100000China School of Computer and Control Engineering University of Chinese Academy of SciencesBeijing 100190China

The dataflow architecture,which is characterized by a lack of a redundant unified control logic,has been shown to have an advantage over the control-flow architecture as it improves the computational performance and power efficiency,especially of applications used in high-performance computing(HPC).Importantly,the high computational efficiency of systems using the dataflow architecture is achieved by allowing program kernels to be activated in a simultaneous ***,a proper acknowledgment mechanism is required to distinguish the data that logically belongs to different *** solutions include the tagged-token matching mechanism in which the data is sent before acknowledgments are received but retried after rejection,or a handshake mechanism in which the data is only sent after acknowledgments are ***,these mechanisms are characterized by both inefficient data transfer and increased area *** performance of the dataflow architecture depends on the efficiency of data *** order to optimize the efficiency of data transfer in existing dataflow architectures with a minimal increase in area and power cost,we propose a Look-Ahead Acknowledgment(LAA)*** accelerates the execution flow by speculatively acknowledging ahead without *** simulation analysis based on a handshake mechanism shows that our LAA increases the average utilization of computational units by 23.9%,with a reduction in the average execution time by 17.4%and an increase in the average power efficiency of dataflow processors by 22.4%.Crucially,our novel approach results in a relatively small increase in the area and power consumption of the on-chip logic of less than 0.9%.In conclusion,the evaluation results suggest that Look-Ahead Acknowledgment is an effective improvement for data transfer in existing dataflow architectures.

关键词： dataflow model control-flow model high-performance computing application data transfer power efficiency

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