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22nd Chinese National Conference on Computational Linguistics, CCL 2023

作者： Xi, Zhiheng Zheng, Rui Gui, Tao School of Computer Science Fudan University Shanghai China Shanghai Key Laboratory of Intelligent Information Processing Shanghai China Institute of Modern Languages and Linguistics Fudan University Shanghai China

ISBN: (纸本)9781713889915

Recent months have witnessed significant progress in the field of large language models (LLMs). Represented by ChatGPT and GPT-4, LLMs perform well in various natural language processing tasks and have been applied to many downstream applications to facilitate people’s lives. However, there still exist safety and ethical concerns. Specifically, LLMs suffer from social bias, robustness problems, and poisoning issues, all of which may induce LLMs to spew harmful contents. We propose this tutorial as a gentle introduction to the safety and ethical issues of LLMs. © 2023 China National Conference on Computational Linguistics.

关键词： Computational linguistics

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Mcc: a predictable and scalable massive client load generator 2nd

Mcc: a predictable and scalable massive client load generato...

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2nd International Symposium on Benchmarking, Measuring, and Optimization, Bench 2019

作者： Wu, Wenqing Feng, Xiao Zhang, Wenli Chen, Mingyu State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Science Beijing China University of Chinese Academy of Sciences Beijing China Peng Cheng Laboratory Shenzhen China

ISBN: (纸本)9783030495558

The network load generators are widely used by network researchers to analyze link bandwidth, evaluate network performance and test device capabilities. Data center and IoT networks are quickly evolving and we desire to get a load generator that can precisely generate flow-level workload with high-throughput. Often researchers choose software-based generators because of their flexibility and open-source nature. However, despite the emerging of different solutions, existing software-based flow-level generators have difficulty in generating millions of concurrent TCP connections or achieving one-microsecond precision of packet inter departure time (IDT) which can undermine the correctness of experiments. In this paper, we present a new network load generator, called Massive Client Connections (MCC). MCC is a client load generator which means it performs flow-level load simulation. We separate the control plane from the data plane and design a two-stage timer mechanism to get higher precision. To take full advantage of multicore processors, we utilize the shared-nothing multi-threaded model. Our evaluation demonstrates that MCC generates network load conforming to expected distribution with one-microsecond precision. Moreover, MCC shows definite scalability of throughput in multicore systems. And it is capable of generating more than three million concurrent TCP connections with ten CPU cores. © Springer Nature Switzerland AG 2020.

关键词： Scalability

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High Performance Computing for CS-InSAR Data Processing

High Performance Computing for CS-InSAR Data Processing

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2021 SAR in Big Data Era, BIGSARDATA 2021

作者： Zhang, Weikang You, Haihang Tang, Yixian Wang, Chao Zhang, Hong Chinese Academy of Sciences State Key Laboratory of Computer Architecture Institute of Computing Technology Beijing China Chinese Academy of Sciences Key Laboratory of Digital Earth Science Aerospace Information Research Institute Beijing China

ISBN: (纸本)9781665401234

In this paper, we have developed a highperformance data processing system for big SAR data based on parallel and distributed computation using supercomputer. Aiming at the pipeline of InSAR, we mainly focus on the parallelization and optimization of computing hotspot to accelerate the operation. Furthermore, we take advantage of the technology of distributed computation and multitask automation to get higher performance - process more data in less time. With the high-performance system, we obtained the nationwide measurement of China's surface movement from September 2018 to February 2020 and from January to December in 2020 using 20-30 computing nodes, processing nearly 1PB of data in total. The calculation time of the two images of national scale surface movement is completed in only 7 and 5 days, respectively. © 2021 IEEE.

关键词： Supercomputers

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DC-store: eliminating noisy neighbor containers using deterministic I/O performance and resource isolation 20

DC-store: eliminating noisy neighbor containers using determ...

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Proceedings of the 18th USENIX Conference on File and Storage Technologies

作者： Miryeong Kwon Donghyun Gouk Changrim Lee Byounggeun Kim Jooyoung Hwang Myoungsoo Jung Computer Architecture and Memory Systems Laboratory Korea Advanced Institute of Science and Technology Computer Architecture and Memory Systems Laboratory Samsung

ISBN: (纸本)9781939133120

We propose DC-store, a storage framework that offers deterministic I/O performance for a multi-container execution environment. DC-store's hardware-level design implements multiple NVM sets on a shared storage pool, each providing a deterministic SSD access time by removing internal resource conflicts. In parallel, software support of DC-Store is aware of the NVM sets and enlightens Linux kernel to isolate noisy neighbor containers, performing page frame reclaiming, from peers. We prototype both hardware and software counterparts of DC-Store and evaluate them in a real system. The evaluation results demonstrate that containerized data-intensive applications on DC-Store exhibit 31% shorter average execution time, on average, compared to those on a baseline system.

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Aware: Adaptive Distributed Training with Computation, Communication and Position Awareness for Deep Learning Model 24

Aware: Adaptive Distributed Training with Computation, Commu...

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Zeng, Yan Yi, Guangzheng Yin, Yuyu Wu, Jiyang Xue, Meiting Zhang, Jilin Wan, Jian Zhang, Yunquan School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China School of Cyberspace Hangzhou Dianzi University Hangzhou China Institute of Computing Technology Chinese Academy of Sciences State Key Laboratory of Computer Architecture Beijing China

ISBN: (纸本)9798350319934

The accuracy of the neural networks can usually be improved by increasing the size of the dataset and the layers or operators of the network, as it has strong composability. But, it makes a challenge to train these models efficiently, due to the limited resources of a single device, such as memory, computational resource and so on. For this challenge, it has become an inevitable trend to automatically implement model parallelism across multiple devices. This paper proposes Aware, an adaptive distributed parallel training method, to search distributed parallel strategy automatically for deep learning model. It firstly proposes an operator fusion strategy with computation and communication awareness to simplify the computational graph of deep learning model. And then, it introduces position-aware graph embedding algorithm to extract the structural features of models, which can make the searched parallel strategies to transplant to other networks with similar structures. On this basis, we use reinforcement learning algorithm to search distributed parallel strategy automatically. This paper makes experiments with neural networks such as Inception V3, NMT, GNM, NasNet and ResNet, and uses CIFAR10 and PTB datasets to compare the two methods of Hierarchical and Placeto. The results show that compared with Placeto, Aware achieves up to 5% reductions in runtime. Aware achieves more than 8% reductions in runtime compared with Hierarchical. Moreover, it has better transplantation and generalization capabilities, and supports pre-training for large-scale network parallel strategy search and accelerates convergence. © 2022 IEEE.

关键词： Deep learning

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Fast assembling of neuron fragments in serial 3D sections

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Brain Informatics 2017年第3期4卷 183-186页

作者： Chen, Hanbo Iascone, Daniel Maxim da Costa, Nuno Maçarico Lein, Ed S. Liu, Tianming Peng, Hanchuan Allen Institute for Brain Science SeattleWA United States Cortical Architecture Imaging and Discovery Laboratory Department of Computer Science and Bioimaging Research Center The University of Georgia AthensGA United States Department of Neuroscience Columbia University New YorkNY United States

Reconstructing neurons from 3D image-stacks of serial sections of thick brain tissue is very time-consuming and often becomes a bottleneck in high-throughput brain mapping projects. We developed NeuronStitcher, a software suite for stitching non-overlapping neuron fragments reconstructed in serial 3D image sections. With its efficient algorithm and user-friendly interface, NeuronStitcher has been used successfully to reconstruct very large and complex human and mouse neurons. © 2017, The Author(s).

关键词： Neurons

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Performance Evaluation of Multiple TCP connections in iSCSI

Performance Evaluation of Multiple TCP connections in iSCSI

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IEEE/NASA Goddard Conference on Mass Storage Systems and Technologies, (MSST)

作者： Bhargava K. Kancherla Ganesh M. Narayan K. Gopinath Computer Architecture and Systems Laboratory Department of Computer Science and Automation Indian Institute of Science Bangalore India

Storage area networks (SANs) based on fibre channel have been used extensively in the last decade while iSCSI is fast becoming a serious contender due to its reduced costs and unified infrastructure. This work examines the performance of iSCSI with multiple TCP connections. Multiple TCP connections are often used to realize higher bandwidth but there may be no fairness in how bandwidth is distributed. We propose a mechanism to share congestion information across multiple flows in "Fair-TCP" for improved performance. Our results show that Fair-TCP significantly improves the performance for I/O intensive workloads.

关键词： Throughput TCPIP Wide area networks Storage area networks Encapsulation Storage automation Bandwidth Transport protocols Optical fiber cables computer architecture

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A Benchmark of Ocular Disease Intelligent Recognition: One Shot for Multi-disease Detection 3rd

A Benchmark of Ocular Disease Intelligent Recognition: One S...

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3rd BenchCouncil International Symposium on Benchmarking, Measuring, and Optimizing, Bench 2020

作者： Li, Ning Li, Tao Hu, Chunyu Wang, Kai Kang, Hong College of Computer Science Nankai university Tianjin300350 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Science Beijing100190 China Beijing Shanggong Medical Technology Co. Ltd. Beijing100176 China

ISBN: (纸本)9783030710576

In ophthalmology, early fundus screening is an economic and effective way to prevent blindness caused by ophthalmic diseases. Clinically, due to the lack of medical resources, manual diagnosis is time-consuming and may delay the condition. With the development of deep learning, some researches on ophthalmic diseases have achieved good results, however, most of them are just based on one disease. During fundus screening, ophthalmologists usually give diagnoses of multi-disease on binocular fundus image, so we release a dataset with 8 diseases to meet the real medical scene, which contains 10,000 fundus images from both eyes of 5,000 patients. We did some benchmark experiments on it through some state-of-the-art deep neural networks. We found simply increasing the scale of network cannot bring good results for multi-disease classification, and a well-structured feature fusion method combines characteristics of multi-disease is needed. Through this work, we hope to advance the research of related fields. © 2021, Springer Nature Switzerland AG.

关键词： computer aided diagnosis

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Testable critical path selection considering process variation

Testable critical path selection considering process variati...

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作者： Fu, Xiang Li, Huawei Li, Xiaowei Key Laboratory of Computer System and Architecture Institute of Computing Technology Chinese Academic of Science Beijing 100190 China Graduate University of Chinese Academic of Sciences Beijing 100039 China

Critical path selection is very important in delay testing. Critical paths found by conventional static timing analysis (STA) tools are inadequate to represent the real timing of the circuit, since neither the testability of paths nor the statistical variation of cell delays caused by process variation is considered. This paper proposed a novel path selection method considering process variation. The circuit is firstly simplified by eliminating non-critical edges under statistical timing model, and then divided into sub-circuits, while each sub-circuit has only one prime input (PI) and one prime output (PO). Critical paths are selected only in critical sub-circuits. The concept of partially critical edges (PCEs) and completely critical edges (CCEs) are introduced to speed up the path selection procedure. Two path selection strategies are also presented to search for a testable critical path set to cover all the critical edges. The experimental results showed that the proposed circuit division approach is efficient in path number reduction, and PCEs and CCEs play an important role as a guideline during path selection. Copyright © 2010 The institute of Electronics.

关键词： Delay circuits

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LONGHEADS: Multi-Head Attention is Secretly a Long Context Processor

LONGHEADS: Multi-Head Attention is Secretly a Long Context P...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Lu, Yi Zhou, Xin He, Wei Zhao, Jun Ji, Tao Gui, Tao Zhang, Qi Huang, Xuanjing School of Computer Science Fudan University Shanghai China Institute of Modern Languages and Linguistics Fudan University Shanghai China Key Laboratory of Intelligent Information Processing Fudan University Shanghai China

ISBN: (纸本)9798891761681

Large language models (LLMs) have achieved impressive performance in numerous domains but often struggle to process lengthy inputs effectively and efficiently due to limited length generalization and attention's quadratic computational demands. Many sought to mitigate this by restricting the attention window within the pre-trained length. However, these methods introduce new issues such as ignoring the middle context and requiring additional training. To address these problems, we propose LONGHEADS, a training-free framework that enhances LLM's long context ability by unlocking multi-head attention's untapped potential. Instead of allowing each head to attend to the full sentence, which struggles with generalizing to longer sequences, we allow each head to process in-distribution length by selecting and attending to important context chunks. To this end, we propose a chunk selection strategy that relies on the inherent correlation between the query and the key representations, efficiently distributing context chunks to different heads. In this way, each head ensures it can effectively process attended tokens within the trained length, while different heads in different layers can collectively process longer contexts. LONGHEADS works efficiently and fits seamlessly with many LLMs that use relative positional encoding. LONGHEADS achieves 100% accuracy at the 128k length on passkey retrieval task, verifying LONGHEADS's efficacy in extending the usable context window for existing models. We release our code at https://***/LuLuLuyi/LongHeads. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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