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Midas Touch: Invalid-Data Assisted Reliability and performance Boost for 3d high-Density Flash

Midas Touch: Invalid-Data Assisted Reliability and Performan...

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IEEE symposium on high-performance computer architecture

作者： Qiao Li Hongyang Dang Zheng Wan Congming Gao Min Ye Jie Zhang Tei-Wei Kuo Chun Jason Xue Xiamen Key Laboratory of Intelligent Storage and Computing Xiamen University City University of Hong Kong Peking University National Taiwan University Mohamed bin Zayed University of Artificial Intelligence

high-density 3D NAND flash like QLC (Quad-Level Cell) is prevailing in providing large capacities for data-intensive applications. Because of the structure limitation, a two-step programming with a specific sequence is adopted in 3D QLC flash, where data could become invalid between the two programming steps. This is called invalid programming, as the second-step programming is conducted on partially-invalid wordlines (WLs). By exploiting this phenomenon, this work proposes invalid-data assisted strategies for performance and reliability boosting of valid data in 3D QLC-based flash storage systems. We first propose a high-efficiency re-programming (RP) scheme to reprogram the valid data and a high-reliability not-programming (NP) scheme to program data on the partially-invalid WLs. An adaptive data allocation (ADA) strategy for data management between the SLC and QLC regions is further introduced to reduce the occurrence of invalid programming. The simulator-based experiments show the proposed RP scheme combined with ADA can reduce the execution time for programming by 13.51%, on average. Through real-device evaluations, we present that the NP scheme can reduce the bit error rate of NP-programmed data by 32.8% of the worst page type, thus improving overall reliability, which translates to 12% reduction in refreshing overheads and 30% lifetime extension, on average. Besides, the NP scheme with ADA averagely reduces energy consumption by 4.8%.

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Optimizing General Matrix Multiplications on Modern Multi-core DSPs

Optimizing General Matrix Multiplications on Modern Multi-co...

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international symposium on Parallel and Distributed Processing (IPDPS)

作者： Kainan Yu Xinxin Qi Peng Zhang Jianbin Fang Dezun Dong Ruibo Wang Tao Tang Chun Huang Yonggang Che Zheng Wang School of Computer Science and Technology National University of Defense Technology Changsha China School of Information Science and Technology Northwest University Xi’an China

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

General Matrix Multiplication (GEMM) is a key subprogram in high-performance computing (HPC) and deep learning workloads. With the rising significance of power and energy consumption in HPC systems, accelerators based on Digital Signal Processors (DSPs) have been integrated into general-purpose HPC systems. Due to the architecture disparities, the GEMM optimization techniques used on conventional multi-core CPUs and GPGPUs are not always applicable to DSPs. This paper shares our experience in optimizing GEMM on multi-core GPDSPs, using a CPU-DSP processor as a case study. Our approach employs a range of techniques to optimize performance for DSP architectures. These include data partitioning, three-level pipelining, dedicated micro-kernel design, and improved vector reduction. These optimizations maximize the overlap between computation and communication while fully exploiting the capabilities of floating-point arithmetic units to achieve high performance. Our experimental results demonstrate that the performance attained by our optimization is up to 96% of the theoretical peak performance of the hardware.

关键词： Single instruction multiple data Shape high performance computing Digital signal processors computer architecture Vectors Hardware

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An Incremental Multi-Level, Multi-Scale Approach to Assessment of Multifidelity HPC Systems

An Incremental Multi-Level, Multi-Scale Approach to Assessme...

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high performance computing, Networking, Storage and Analysis, SC-W: workshops of the international Conference for

作者： Shilpika Shilpika Bethany Lusch Venkatram Vishwanath Michael E. Papka Argonne Leadership Computing Facility Argonne National Laboratory Department of Computer Science Argonne Leadership Computing Facility Argonne National Laboratory University of Illinois Chicago

ISBN: (数字)9798350355543

ISBN: (纸本)9798350355550

With the growing complexity in architecture and the size of large-scale computing systems, monitoring and analyzing system behavior and events has become daunting. Monitoring data amounting to terabytes per day are collected by sensors housed in these massive systems at multiple fidelity levels and varying temporal resolutions. In this work, we develop an incremental version of multiresolution dynamic mode decomposition (mrDMD), which converts high-dimensional data to spatial-temporal patterns at varied frequency ranges. Our incremental implementation of the mrDMD algorithm (I-mrDMD) promptly reveals valuable information in the massive environment log dataset, which is then visually aligned with the processed hardware and job log datasets through our generalizable rack visualization using D3 visualization integrated into the Jupyter Notebook interface. We demonstrate the efficacy of our approach with two use scenarios on a real-world dataset from a Cray XC40 supercomputer, Theta.

关键词： Heuristic algorithms Visual analytics Layout Time series analysis Data visualization Supercomputers Hardware Sensors Spatial resolution Monitoring

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CAMEL: Co-Designing AI Models and eDRAMs for Efficient On-Device Learning

CAMEL: Co-Designing AI Models and eDRAMs for Efficient On-De...

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IEEE symposium on high-performance computer architecture

作者： Sai Qian Zhang Thierry Tambe Nestor Cuevas Gu-Yeon Wei David Brooks Department of Electrical Engineering and Department of Computer Science New York University John A. Paulson School of Engineering and Applied Sciences Harvard University

On-device learning allows AI models to adapt to user data, thereby enhancing service quality on edge platforms. However, training AI on resource-limited devices poses significant challenges due to the demanding computing workload and the substantial memory consumption and data access required by deep neural networks (DNNs). To address these issues, we propose utilizing embedded dynamic random-access memory (eDRAM) as the primary storage medium for transient training data. In comparison to static random-access memory (SRAM), eDRAM provides higher storage density and lower leakage power, resulting in reduced access cost and power leakage. Nevertheless, to maintain the integrity of the stored data, periodic power-hungry refresh operations could potentially degrade system performance. To minimize the occurrence of expensive eDRAM refresh operations, it is beneficial to shorten the lifetime of stored data during the training process. To achieve this, we adopt the principles of algorithm and hardware co-design, introducing a family of reversible DNN architectures that effectively decrease data lifetime and storage costs throughout training. Additionally, we present a highly efficient on-device training engine named CAMEL, which leverages eDRAM as the primary on-chip memory. This engine enables efficient on-device training with significantly reduced memory usage and off-chip DRAM traffic while maintaining superior training accuracy. We evaluate our CAMEL system on multiple DNNs with different datasets, demonstrating a 2.5× speedup of the training process and 2.8× training energy savings than the other baseline hardware platforms.

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Efficient Multi-dimensional Compression for Network-edge Classification 24

Efficient Multi-dimensional Compression for Network-edge Cla...

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2024 international symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile computing, MobiHoc 2024

作者： Li, Chengzhang Ju, Peizhong Eryilmaz, Atilla Shroff, Ness B. Department of Electrical and Computer Engineering The Ohio State University ColumbusOH United States

ISBN: (纸本)9798400705212

The widespread adoption of low-cost resource-constrained edge devices and high-performance expensive servers necessitates shifting the complexity burden from edge devices to servers. However, in many applications such as image classification, it is often impractical and communication expensive to transmit full information without any form of compression. To address this issue, this paper introduces a neural network (NN)-based compression technique tailored for resource-constrained edge devices for classification at the network edge. The core idea involves simultaneously training a shallow neural network to-be-implemented by the devices and a deep neural network to-be-implemented by the server. To adapt to the time-varying channel conditions, the compression algorithm at the device side must be able to handle multiple output dimensions. To address this issue, we develop two multi-dimensional compression strategies: the multiple codebook approach, using separate NNs for various dimensions, and the single codebook approach, utilizing one NN for all dimensions. The single codebook approach substantially reduces the storage demands on the device, offering a viable solution for low-cost edge devices. Our analysis offers a theoretical performance guarantee, highlighting that the accuracy of the single codebook approach is comparable to that of the multiple codebook strategy. Through empirical evaluations on real-world datasets, we demonstrate that the single codebook approach achieves near-equivalent performance to the accuracy multiple codebook alternative. © 2024 Copyright held by the owner/author(s).

关键词： Dimensionality reduction

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Multi-objective Quantum Optimization based on Bayesian Approach for computing Resource Assignment Problems

Multi-objective Quantum Optimization based on Bayesian Appro...

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international symposium on computing and Networking workshops (CANDARW)

作者： Toyoki Kosuda Morikazu Nakamura Tadashi Shiroma Takayuki Nakachi Computer Sciences and Intelligent Systems Faculty of Eng. University of the Ryukyus Japan Information Technology Center University of the Ryukyus Japan

ISBN: (数字)9798331505349

ISBN: (纸本)9798331505356

In quantum optimization approaches, such as Quantum Approximate Optimization Algorithms (QAOA) and quantum annealing, the optimal solution is obtained by minimizing an energy function that encapsulates the optimization problem. In the case of multi-objective optimization problems, the trade-offs between competing objective functions are managed by adjusting a set of weight parameters. This paper investigates two parameter-tuning techniques, a grid search and Bayesian optimization, applied to a computational resource allocation problem with multiple objective functions. We compare the performance of these methods by evaluating the efficiency, diversity, and uniformity of the Pareto solutions derived from the optimization results.

关键词： Annealing Conferences Linear programming Search problems Quantum annealing Approximation algorithms Bayes methods Resource management Optimization

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Preparing for HPC on RISC-V: Examining Vectorization and Distributed performance of an Astrophysics Application with HPX and Kokkos

Preparing for HPC on RISC-V: Examining Vectorization and Dis...

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high performance computing, Networking, Storage and Analysis, SC-W: workshops of the international Conference for

作者： Patrick Diehl Panagiotis Syskakis Gregor Daiß Steven R. Brandt Alireza Kheirkhahan Srinivas Yadav Singanaboina Dominic Marcello Chris Taylor John Leidel Hartmut Kaiser Applied Computer Science (CCS-7) Los Alamos National Laboratory Los Alamos NM U.S.A. Department of Physics and Astronomy Louisiana State University Baton Rouge LA U.S.A. LSU Center for Computation & Technology Louisiana State University Baton Rouge LA U.S.A. IPVS University of Stuttgart Stuttgart Germany Tactical Computing Labs Lindsay TX U.S.A. Department of Computer Science Louisiana State University Baton Rouge LA U.S.A.

ISBN: (数字)9798350355543

ISBN: (纸本)9798350355550

In recent years, interest in RISC-V computing architectures has moved from academic to mainstream, especially in the field of high performance computing where energy limitations are increasingly a concern. As of this year, the first single board RISC-V CPUs implementing the finalized ratified vector specification are being released. The RISC-V vector specification follows in the tradition of vector processors found in the CDC STAR-100, the Cray-1, the Convex C-Series, and the NEC SX machines and accelerators. The family of vector processors offers support for variable-length array processing as opposed to the fixed-length processing functionality offered by SIMD. Vector processors offer opportunities to perform vector-chaining which allows temporary results to be used without the need to resolve memory *** this work, we use the Octo-Tiger multi-physics, multi-scale, 3D adaptive mesh refinement astrophysics application to study these early RISC-V chips with vector machine support. We report on our experience in porting this modern C++ code (which is built upon several open-source libraries such as HPX and Kokkos) to RISC-V. In addition, we show the impact of the RISC-V Vector extension on a RISC-V single board computer by implementing the std :: experimental:simd interface and integrating it with our code. We also compare the application’s performance, scalability, and power consumption on desktop-grade RISC-V computer to an A64FX *** results presented in this paper are part of a longer-term evaluation of RISC-V’s viability for HPC applications.

关键词： Power demand Astrophysics high performance computing Europe computer architecture Hardware Vectors Supercomputers Libraries Vector processors

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FAFMOTS: A Fast and Anchor Free Method for Online Joint Multi-Object Tracking and Segmentation 21

FAFMOTS: A Fast and Anchor Free Method for Online Joint Mult...

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21st IEEE international symposium on Mixed and Augmented Reality (ISMAR)

作者： Li, Shuman Feng, Weijiang Yang, Longqi Yang, Wenjing Yang, Shaowu Lan, Long Natl Univ Def Technol Inst Quantum Informat Coll Comp Sci & Technol Changsha Peoples R China Natl Univ Def Technol State Key Lab High Performance Comp Coll Comp Sci & Technol Changsha Peoples R China PLA Informat Engn Univ Henan Key Lab Quantum Informat & Cryptog Zhengzhou Peoples R China Def Innovat Inst Changsha Peoples R China

ISBN: (数字)9781665453653

ISBN: (纸本)9781665453653

Fast object tracking system is important to Augmented Reality (AR). However, tracking multiple objects fast and accurately is not a trivial task in computer vision, especially when the objects become crowded and frequently occluded. In this paper, we design an efficient segmentation model along with the tracking and carefully study the contributions of pixel-level appearance modeling in the presence of occlusions. Different from previous joint tracking and segmentation methods that depend on bounding boxes for instance segmentation, we proposed an anchor free model to speed the object mask estimation. Specifically, our proposed approach jointly addresses instance segmentation and tracking with a single convolutional network for the first time, and generates object masks to explicitly separate overlapping objects in tracking. As pointed out by several previous works, the goals of tracking and segmentation are not always consistent, we deliberately design three homogeneous branches in our network to balance the competitive three functional parts of objectness, masks, and re-identification simultaneously and constrain them in a comparable scale. With the effective three functional parts, our method could run fast for the time-sensitive tracking task and meanwhile relieve the effects of appearance noise brought by the occlusions. Experimental results on the MOTS benchmark demonstrate the effectiveness of the proposed method.

关键词： computing methodologies-Artificial intelligence-computer vision-Tracking computing methodologies-Artificial intelligence-computer vision-Video segmentation

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Hands-On Learning: Teaching Parallel and Distributed computing through Unplugged Activities in Undergraduate CS Courses 24

Hands-On Learning: Teaching Parallel and Distributed Computi...

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Proceedings of the SC '24 workshops of the international Conference on high performance computing, Network, Storage, and Analysis

作者： Anurag Dasgupta Venkat Margapuri Simon Shamoun Shubbhi Taneja Matthew Toups Computer Science and Engineering Technology Valdosta State University Valdosta GA Department of Computing Sciences Villanova University Villanova PA Department of Computer Science Hofstra University Hempstead NY Department of Computer Science Worcester Polytechnic Institute Worcester Massachusetts Computer Science Department Tulane University New Orleans LA

ISBN: (纸本)9798350355543

The authors present and evaluate an unplugged activity to introduce parallel computing concepts to undergraduate students. Students in five CS classrooms used a deck of playing cards in small groups to consider how parallelization can improve performance and how improvement decreases with increased parallelization. Before and after the activity, students took a short survey about their solution and their ideas about parallelism. The authors carried out this activity in seven courses at five institutions in the 2023-2024 academic year. The results showed that students had an increased appreciation for parallelization and this type of activity.

关键词： CS

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OPTIMAL ASSIGNMENT STRATEGY FOR DYNAMIC WORKFLOW OF REMOTE SENSING BIG DATA PROCESSING

OPTIMAL ASSIGNMENT STRATEGY FOR DYNAMIC WORKFLOW OF REMOTE S...

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IEEE international Geoscience and Remote Sensing symposium (IGARSS)

作者： Zhang, Sheng Xue, Yong Ming, Yang Zhang, Xiaopeng Jin, Chunlin Jiang, Xingxing Zhou, Xiran China Univ Min & Technol Sch Environm & Spatial Informat Xuzhou 221116 Jiangsu Peoples R China China Univ Min & Technol Artificial Intelligence Res Inst Xuzhou 221116 Jiangsu Peoples R China Shandong Prov Inst Land Surveying & Mapping Jinan 250013 Shandong Peoples R China

ISBN: (数字)9781665427920

ISBN: (纸本)9781665427920

The advent of the era of Remote Sensing Big Data has produced a large number of processing and analysis tasks, which require powerful computing capabilities to support. The computational efficiency of distributed computer clusters which are the most commonly used parallel computing architecture for high performance computing can be significantly improved through an effective task scheduling strategy. In this paper, in order to improve data computing efficiency, we propose a dynamic load balancing strategy for remote sensing data processing workflow tasks based on the Hungarian algorithm for heterogeneous distributed computing clusters. We also compare this strategy with the classic load balancing algorithm. We find that the speed-up effect of the strategy proposed in this paper is better, and the speedups become more pronounced as the number of tasks increases.

关键词： workflow heterogeneous computing platform Hungarian algorithm optimal assignment

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