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Accelerating the cryo-EM structure determination in RELION on GPU cluster

Frontiers of Computer Science 2022年第3期16卷 21-39页

作者： Xin YOU Hailong YANG Zhongzhi LUAN Depei QIAN Sino-German Joint Software Institute School of Computer Science and EngineeringBeihang UniversityBeijing 100191China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214125

The cryo-electron microscopy(cryo-EM)is one of the most powerful technologies available today for structural *** RELION(Regularized Likelihood Optimization)implements a Bayesian algorithm for cryo-EM structure determination,which is one of the most widely used software in this *** researchers have devoted effort to improve the performance of RELION to satisfy the analysis for the ever-increasing volume of *** this paper,we focus on performance analysis of the most time-consuming computation steps in RELION and identify their performance bottlenecks for specific *** propose several performance optimization strategies to improve the overall performance of RELION,including optimization of expectation step,parallelization of maximization step,accelerating the computation of symmetries,and memory affinity *** experiment results show that our proposed optimizations achieve significant speedups of RELION across representative *** addition,we perform roofline model analysis to understand the effectiveness of our optimizations.

关键词： cryo-EM structure determination performance optimization GPU acceleration RELION

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Towards optimized tensor code generation for deep learning on sunway many-core processor

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Frontiers of Computer Science 2024年第2期18卷 1-15页

作者： Mingzhen LI Changxi LIU Jianjin LIAO Xuegui ZHENG Hailong YANG Rujun SUN Jun XU Lin GAN Guangwen YANG Zhongzhi LUAN Depei QIAN State Key Laboratory of Software Development Environment Beijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China National University of Singapore Singapore 119077Singapore State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214000China Science and Technology on Special System Simulation Laboratory Beijing Simulation Center Beijing 100854China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China

The flourish of deep learning frameworks and hardware platforms has been demanding an efficient compiler that can shield the diversity in both software and hardware in order to provide application *** the existing deep learning compilers,TVM is well known for its efficiency in code generation and optimization across diverse hardware *** the meanwhile,the Sunway many-core processor renders itself as a competitive candidate for its attractive computational power in both scientific computing and deep learning *** paper combines the trends in these two ***,we propose swTVM that extends the original TVM to support ahead-of-time compilation for architecture requiring cross-compilation such as *** addition,we leverage the architecture features during the compilation such as core group for massive parallelism,DMA for high bandwidth memory transfer and local device memory for data locality,in order to generate efficient codes for deep learning workloads on *** experiment results show that the codes generated by swTVM achieve 1.79x improvement of inference latency on average compared to the state-of-the-art deep learning framework on Sunway,across eight representative *** work is the first attempt from the compiler perspective to bridge the gap of deep learning and Sunway processor particularly with productivity and efficiency in *** believe this work will encourage more people to embrace the power of deep learning and Sunwaymany-coreprocessor.

关键词： sunway processor deep learning compiler code generation performance optimization

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Haica: A High Performance computing & Artificial Intelligence Fused computing Architecture 1

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22nd International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2022

作者： Chen, Zhengbo Zheng, Fang Guo, Feng Yu, Qi Chen, Zuoning Information Engineering University Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China Chinese Academy of Engineering Beijing China

ISBN: (数字)9783031226779

ISBN: (纸本)9783031226762

In recent years, HPC and AI fusion, which refers to applying AI technology to traditional HPC applications, has become a new trend. HPC and AI fusion requires supports for multiple precisions used in both domains. While, supporting multiple precisions in a single computing unit is not easy. Prior research typically targets on modifying a high-precision FMA to support low precisions. However, such efforts suffer from disadvantages of increased latency and limited compute throughput for low-precision operations, high area and power overhead, and limited support of new data formats appeared in AI domain. To address this issue, we propose Haica, a double-precision FMA and low-precision systolic array fused architecture, to achieve HPC and AI fusion. Our work has two innovations. First, we propose a low-cost multiple-low-precision FMA by taking advantage of the commonality among FP16, BF16, and TF32. Second, inspired by the idea of splicing high precision with low precision, we replace the multiply and merge modules in a double-precision FMA with a modified systolic array that is composed of the proposed low-precision FMAs. We implement the logic design of Haica using RTL and evaluate its overhead. Results show that compared to the naive combination of a double-precision FMA and single-half-mixed-precision systolic array, Haica provides extra support for BF16 and TF32 with only 7.87% area and 33.26% power overhead. This demonstrates that Haica achieves HPC and AI fusion in a cost-effective manner. © 2023, Springer Nature Switzerland AG.

关键词： Artificial intelligence

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Multiuser Computation Offloading for Long-Term Sequential Tasks in Mobile Edge computing Environments

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Tsinghua Science and Technology 2023年第1期28卷 93-104页

作者： Huanhuan Xu Jingya Zhou Wenqi Wei Baolei Cheng School of Computer Science and Technology Soochow UniversitySuzhou 215006China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214125China Provincial Key Laboratory for Computer Information Processing Technology Soochow UniversitySuzhou 215006China School of Computer Science Georgia Institute of TechnologyAtlantaGA 30332USA

Mobile edge computing has shown its potential in serving emerging latency-sensitive mobile applications in ultra-dense 5G networks via offloading computation workloads from the remote cloud data center to the nearby network ***,current computation offloading studies in the heterogeneous edge environment face multifaceted challenges:Dependencies among computational tasks,resource competition among multiple users,and diverse long-term *** applications typically consist of several functionalities,and one huge category of the applications can be viewed as a series of sequential *** this study,we first proposed a novel multiuser computation offloading framework for long-term sequential ***,we presented a comprehensive analysis of the task offloading process in the framework and formally defined the multiuser sequential task offloading ***,we decoupled the long-term offloading problem into multiple single time slot offloading problems and proposed a novel adaptive method to solve *** further showed the substantial performance advantage of our proposed method on the basis of extensive experiments.

关键词： mobile edge computing sequential tasks computation offloading dependency

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An algorithm/hardware co-optimized method to accelerate CNNs with compressed convolutional weights on FPGA

An algorithm/hardware co-optimized method to accelerate CNNs...

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作者： Shang, Jiangwei Zhang, Zhan Zhang, Kun Li, Chuanyou Qian, Lei Liu, Hongwei School of Computer Science and Technology Harbin Institute of Technology Harbin China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China School of Computer Science and Engineering Southeast University Nanjing China

Convolutional neural networks (CNNs) have shown remarkable advantages in a wide range of domains at the expense of huge parameters and computations. Modern CNNs still tend to be more complex and larger to achieve better inference accuracy. However, the complex and large structures of CNNs could slow down the inference speed. Recently, Compressing the convolutional weights to be sparse by pruning the unimportant parameters has been demonstrated as an efficient way to reduce the computations of CNNs. On the other hand, field-programmable gate arrays (FPGAs) have been a popular hardware platform to accelerate CNN inference. In this paper, we propose an algorithm/hardware co-optimized method for accelerating CNN inference on FPGAs. For the algorithm, we take advantage of unstructured and structured parameter sparsifying methods to achieve high sparsity and keep the regularity of convolutional weights. Correspondingly, hardware-friendly index representations of sparse convolutional weights are proposed. For the hardware architecture, we propose row-wise input-stationary dataflow, which is tightly coupled with the algorithm. A row-wise computing engine (RConv Engine) is proposed, which is based on the dataflow. Inside the RConv Engine, the scalar-vector structure is applied to implement the basic processing elements (PEs). To flexibly calculate the feature map with various sizes, the PEs are organized in a 2D structure with two work modes. The experimental results demonstrate that our co-optimized method implements high sparsity of convolutional weights, and the computing engine achieves high computation efficiency. Compared with other accelerators, our co-optimized method implements a 10.9 (Formula presented.) speedup on FPS at most with the highest sparsity of convolutional weights and negligible accuracy loss. © 2024 John Wiley & Sons Ltd.

关键词： Engines

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Research on Fuzzing Technology for JavaScript Engines 21

Research on Fuzzing Technology for JavaScript Engines

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5th International Conference on Computer Science and Application engineering, CSAE 2021

作者： Tian, Ye Qin, Xiaojun Gan, Shuitao State Key Laboratory of Mathematical Engineering and Advanced Computing Zheng zhou Wuxi China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China

ISBN: (纸本)9781450389853

JavaScript engine is the core component of web browsers, whose security issues are one of the critical aspects of the overall Web Eco-Security. Fuzzing technology, as an efficient software testing approach, has been widely applied to detecting vulnerabilities in different JavaScript engines, which is a security research hotspot at present. Based on systematical dissection of existing fuzzing methods, this paper reviews the development and technical ideas of JavaScript Engine Fuzzing combined with taxonomy, proposes a general framework of JavaScript Engine Fuzzing and analyzes the key techniques involved. Finally, we discuss the core issues that restrict efficiency in current research and present an outlook on the future trends of JavaScript Engine Fuzzing.. © 2021 Association for computing Machinery. All rights reserved.

关键词： Engines

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HDBFuzzer-Target-oriented Hybrid Directed Binary Fuzzer 21

HDBFuzzer-Target-oriented Hybrid Directed Binary Fuzzer

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5th International Conference on Computer Science and Application engineering, CSAE 2021

作者： Yu, Yingchao Qin, Xiaojun Gan, Shuitao State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China

ISBN: (纸本)9781450389853

In this paper, we propose a target-oriented hybrid directed binary fuzzer (HDBFuzzer) to solve the vulnerability confirmation problem based on binary code similarity comparison. HDBFuzzer combines macro function level direction fuzzing and micro path-constraint directed solving. For some branches with simple or loose constraints, it still uses directed mutation of the directed fuzzing to penetrate while for some really hard-to-penetrate constraints, it resorts to guided concolic execution. At the same time, in order to improve the efficiency of constraint solving, we propose a constraint solving method based on "path abstraction", which approximates the solution space by the linear expression and generates effective input utilizing the highly-effective sampling method towards the linear space. Then, under the guidance by the directed greybox fuzzing, HDBFuzzer can generate input that can quickly reach the vulnerable code region and finally crash the program under the test to confirm the vulnerability hidden in the binary program. We evaluate HDBFuzzer against AFLGo-B and QSYM on LAVA-M dataset and ten real-world programs, and the results show that HDBFuzzer is superior to AFLGo-B and QSYM on the bug discovery, bug reproduction and target reaching capabilities.. © 2021 Association for computing Machinery. All rights reserved.

关键词： Software testing

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High-performance optimization of SM4-GCM based on FPGA

High-performance optimization of SM4-GCM based on FPGA

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2021 International Conference on advanced computing and Endogenous Security, ICACES 2021

作者： Lv, Shun Sen Li, Bin Chen, Xiaojie Zhou, Qinglei School of Information Engineering Zhengzhou University Zhengzhou China State Key Laboratory of Mathmatical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781665424493

SM4-GCM is an encryption algorithm with authentication function. The algorithm achieves the purpose of data security and information integrity. The SM4-GCM algorithm, implemented using traditional software methods, has low throughput and high resource consumption. In order to further improve the algorithm performance, this paper uses FPGA to optimize the SM4-GCM algorithm to achieve full-pipeline parallel acceleration. Firstly, the SM4 module is optimized using pipelining techniques. Then, the GHASH module is optimized using the Karatsuba algorithm and fast reduction. Finally, a loosely coupled architecture is used to connect various modules with asynchronous FIFOs, which improves the resource utilization and throughput of the FPGA circuit. The experimental results show that the throughput of the optimized SM4-GCM algorithm reaches 28.8 Gbps. It is better than other schemes, has a higher throughput, and meets the actual application requirements. © 2022 IEEE.

关键词： Pipelines

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FirmVulSeeker—BERT and Siamese Network-Based Vulnerability Search for Embedded Device Firmware Images

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Journal on Internet of Things 2022年第1期4卷 1-20页

作者： Yingchao Yu Shuitao Gan Xiaojun Qin State Key Laboratory of Mathematical Engineering and Advanced Computing WuxiJiangsu214083China

In recent years,with the development of the natural language processing(NLP)technologies,security analyst began to use NLP directly on assembly codes which were disassembled from binary executables in order to examine binary similarity,achieved great ***,we found that the existing frameworks often ignored the complex internal structure of instructions and didn’t fully consider the long-term dependencies of *** this paper,we propose firmVulSeeker—a vulnerability search tool for embedded firmware images,based on BERT and Siamese *** first builds a BERT MLM task to observe and learn the semantics of different instructions in their context in a very large unlabeled binary ***,a finetune mode based on Siamese network is constructed to guide training and matching semantically similar functions using the knowledge learned from the first ***,it will use a function embedding generated from the fine-tuned model to search in the targeted corpus and find the most similar function which will be confirmed whether it’s a real vulnerability *** evaluate the accuracy,robustness,scalability and vulnerability search capability of *** show that it can greatly improve the accuracy of matching semantically similar functions,and can successfully find more real vulnerabilities in real-world firmware than other tools.

关键词： Embedded device firmware vulnerability search BERT siamese network

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Focus, Distinguish, and Prompt: Unleashing CLIP for Efficient and Flexible Scene Text Retrieval 24

Focus, Distinguish, and Prompt: Unleashing CLIP for Efficien...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zeng, Gangyan Zhang, Yuan Wei, Jin Yang, Dongbao Zhang, Peng Gao, Yiwen Qin, Xugong Zhou, Yu School of Cyber Science and Engineering Nanjing University of Science and Technology Nanjing China State Key Laboratory of Media Convergence and Communication Communication University of China Beijing China Lenovo Research Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China Laboratory for Advanced Computing and Intelligence Engineering Wuxi China TMCC College of Computer Science Nankai University Tianjin China

ISBN: (纸本)9798400706868

Scene text retrieval aims to find all images containing the query text from an image gallery. Current efforts tend to adopt an Optical Character Recognition (OCR) pipeline, which requires complicated text detection and/or recognition processes, resulting in inefficient and inflexible retrieval. Different from them, in this work we propose to explore the intrinsic potential of Contrastive Language-Image Pre-training (CLIP) for OCR-free scene text retrieval. Through empirical analysis, we observe that the main challenges of CLIP as a text retriever are: 1) limited text perceptual scale, and 2) entangled visual-semantic concepts. To this end, a novel model termed FDP (Focus, Distinguish, and Prompt) is developed. FDP first focuses on scene text via shifting the attention to the text area and probing the hidden text knowledge, and then divides the query text into content word and function word for processing, in which a semantic-aware prompting scheme and a distracted queries assistance module are utilized. Extensive experiments show that FDP significantly enhances the inference speed while achieving better or competitive retrieval accuracy compared to existing methods. Notably, on the IIIT-STR benchmark, FDP surpasses the state-of-the-art model by 4.37% with a 4 times faster speed. Furthermore, additional experiments under phrase-level and attribute-aware scene text retrieval settings validate FDP's particular advantages in handling diverse forms of query text. The source code will be available at https://***/Gyann-z/FDP. © 2024 ACM.

关键词： Semantics

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