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Affordable HPC: Leveraging Small Clusters for Big Data and Graph Computing

Affordable HPC: Leveraging Small Clusters for Big Data and G...

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parallel Computing and distributed systems (PCDS), international symposium on

作者： Ruilong WU Yisu Wang Dirk KUTSCHER IOT Thrust INFO Hub HKUST(GZ) Guangzhou China

ISBN: (数字)9798350349658

ISBN: (纸本)9798350349665

This study explores strategies for academic re-searchers to optimize computational resources within limited budgets, focusing on building small, efficient computing clusters. It delves into the comparative costs of purchasing versus renting servers, guided by market research and economic theories on tiered pricing. The paper offers detailed insights into the selection and assembly of hardware components such as CPUs, GPUs, and motherboards tailored to specific research needs. It introduces innovative methods to mitigate the performance issues caused by PCIe switch bandwidth limitations in order to enhance GPU task scheduling. Furthermore, a Graph Neural Network (GNN) framework is proposed to analyze and optimize parallelism in computing networks.

关键词： Costs Systematics Network topology Switches parallel processing Graph neural networks Hardware Topology Computational efficiency distributed computing

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distributed Deep Learning Using Volunteer Computing-Like Paradigm

Distributed Deep Learning Using Volunteer Computing-Like Par...

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35th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Atre, Medha Jha, Birendra Rao, Ashwini Eydle Inc Los Angeles CA 90803 USA

ISBN: (纸本)9781665435772

Use of Deep Learning (DL) in commercial applications such as image classification, sentiment analysis and speech recognition is increasing. When training DL models with large number of parameters and/or large datasets, cost and speed of training can become prohibitive. distributed DL training solutions that split a training job into subtasks and execute them over multiple nodes can decrease training time. However, the cost of current solutions, built predominantly for cluster computing systems, can still be an issue. In contrast to cluster computing systems, Volunteer Computing (VC) systems can lower the cost of computing, but applications running on VC systems have to handle fault tolerance, variable network latency and heterogeneity of compute nodes, and the current solutions are not designed to do so. We design a distributed solution that can run DL training on a VC system by using a data parallel approach. We implement a novel asynchronous SGD scheme called VC-ASGD suited for VC systems. In contrast to traditional VC systems that lower cost by using untrustworthy volunteer devices, we lower cost by leveraging preemptible computing instances on commercial cloud platforms. By using preemptible instances that require applications to be fault tolerant, we lower cost by 70-90% and improve data security.

关键词： distributed systems distributed training deep learning volunteer computing ASGD

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Optimizing Forward Wavefield Storage Leveraging High -Speed Storage Media

Optimizing Forward Wavefield Storage Leveraging High -Speed ...

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1st international Conference on Smart Energy systems and Artificial Intelligence (SESAI)

作者： Speglich, Joao Kukreja, Navjot Bisbas, George Saraiva, Atila Huckelheim, Jan Luporini, Fabio Washbourne, John SENAI CIMATEC Supercomp Ctr Salvador BA Brazil Univ Liverpool Liverpool Merseyside England Imperial Coll London London England Argonne Natl Lab 9700 S Cass Ave Argonne IL 60439 USA Devito Codes London England Chevron Tech Ctr Houston TX USA

ISBN: (纸本)9798350364613;9798350364606

Full-Waveform Inversion (FWI) is a commonly used adjoint-based optimization technique in geosciences. Similar to other adjoint-based optimizations, MI involves gradient calculations that impose a substantial memory footprint. This memory footprint is often considered the primary bottleneck in this FWI process and has been the subject of extensive research. Previous studies have explored various approaches to mitigate this issue, including optimal checkpointing strategies, lossy compression, subsampling in time, and other methods aimed at reducing the memory requirements. These investigations have typically operated under the assumption that writing the data to disk would result in significant and unacceptable performance degradation, leading to compromises in precision or increased computational costs to conserve memory resourcesin this work, we challenge this assumption in light of recent advancements in high -bandwidth storage, particularly the utilization of Non-Volatile Memory Express solid-state drives (NVMe SSDs). We provide empirical evidence that, with specific combinations of modern hardware, it is indeed feasible to write temporary data to disk during the forward propagation phase and subsequently read it back during the adjoint computation without experiencing substantial performance degradation.

关键词： waveform inversion Adjoint-based optimization Gradient calculations Memory footprint Large-scale inverse problems parallel computing Non-volatile memory express solid-state drives (NVMe SSDs) high-speed storage

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Integration Framework for Online Thread Throttling with Thread and Page Mapping on NUMA systems

Integration Framework for Online Thread Throttling with Thre...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Janaina Schwarzrock Arthur F. Lorenzon Samuel Xavier de Souza Antonio Carlos S. Beck Federal University of Rio Grande do Sul Brazil Federal University of Rio Grande do Norte Brazil

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

Non-Uniform Memory Access (NUMA) systems are preva-lent in HPC, where optimal thread and page placement are crucial for enhancing performance and minimizing energy us-age [1]–[3]. Moreover, considering that NUMA systems have hardware support for a large number of hardware threads and many parallel applications have limited scalability, throttling the number of active threads may bring further improvements [4]–[6]. However, the optimal configuration (thread mapping, page mapping, number of threads) for energy and performance, quantified by the Energy-Delay Product (EDP), varies with the system hardware, application, input set, and even during execution [1], [4], [6], [7]. Only online optimization approaches can easily adapt to these changes.

关键词： Concurrent computing distributed processing Instruction sets Scalability Conferences Hardware Optimization

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ReSLB: Load Balanced Workflow for distributed Deep Learning Mass Spectrometry Database 24

ReSLB: Load Balanced Workflow for Distributed Deep Learning ...

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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： Mocheng, Li Yang, Liu Yang, Ou Zhiguang, Chen Nong, Xiao Tao, Chen College of Computer National University of Defense Technology Institute for Quantum Information State Key Laboratory of High Performance Computing Changsha China College of Computer National University of Defense Technology Changsha China School of Computer Sun Yat-sen University Guangzhou China Beijing China

ISBN: (纸本)9798350319934

The proteomics data analysis pipeline based on the shotgun method requires efficient data processing methods. The parallel algorithm of mass spectrometry database search faces the problems of rapidly expanding database size but low scalability. The heterogeneous database search algorithm based on deep learning is an effective way to solve this problem. Still, the deep learning-based distributed parallel database search algorithm is lacking. This paper analyzes the database searching computational load using a neural network and designs ReSLB, a workflow for the restricted search of mass spectrometry data based on GPU cluster and neural network scoring algorithm. This work aims to ensure the high scalability of future deep learning-based mass spectrometry distributed databases. In the performance estimation of 256 GPUs, the load imbalance of less than 30% and the parallel efficiency of 60% are achieved. Compared with state-of-the-art, the time cost is reduced by 75%, and the parallel efficiency from 1 to 256 GPUs is 3.6x higher than that. © 2022 IEEE.

关键词： Mass spectrometry

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CODBS: A cascading oblivious search protocol optimized for real-world relational database indexes 40

CODBS: A cascading oblivious search protocol optimized for r...

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40th international symposium on Reliable distributed systems (SRDS)

作者： Pontes, Rogerio Portela, Bernardo Barbosa, Manuel Vilaca, Ricardo Univ Porto Porto Portugal Univ Minho Braga Portugal INESC TEC Porto Portugal NOVA LINCS Costa Da Caparica Portugal

ISBN: (纸本)9781665438193

Encrypted databases systems and searchable encryption schemes still leak critical information (e.g.: access patterns) and require a choice between privacy and efficiency. We show that using ORAM schemes as a black-box is not a panacea and that optimizations are still possible by improving the data structures. We design an ORAM-based secure database that is built from the ground up: we replicate the typical data structure of a database system using different optimized ORAM constructions and derive a new solution for oblivious searches on databases. Our construction has a lower bandwidth overhead than state-of-the-art ORAM constructions by moving client-side computations to a proxy with an intermediate (rigorously defined) level of trust, instantiated as a server-side isolated execution environment. We formally prove the security of our construction and show that its access patterns depend only on public information. We also provide an implementation compatible with SQL databases (PostgresSQL). Our system is 1.2 times to 4 times faster than state-of-the-art ORAM-based solutions.

关键词： Data privacy Protocols Bandwidth Relational databases Forestry Reliability theory Data structures

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A Message Passing Interface Library for High-Level Synthesis on Multi-FPGA systems 15

A Message Passing Interface Library for High-Level Synthesis...

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15th IEEE international symposium on Embedded Multicore/Many-Core systems-on-Chip, MCSoC 2022

作者： Hironaka, Kazuei Iizuka, Kensuke Amano, Hideharu Keio University Dept. of Information and Computer Science Yokohama Japan

ISBN: (纸本)9781665464994

One obstacle to application development on multi-FPGA systems with high-level synthesis (HLS) is a lack of support for a programming interface. Implementing and debugging an application on multiple FPGA boards is difficult without a standard interface. Message Passing Interface (MPI) is a standard parallel programming interface commonly used in distributed memory systems. This paper presents a tool-independent MPI library called FiC-MPI that can be used in HLS for multi-FPGA systems in which each FPGA node is connected directly. By using FiC-MPI, various parallel software, including a general-purpose benchmark, can be easily implemented. FiC-MPI was implemented and evaluated on the M-KUBOS cluster consisting of Zynq MPSoC boards connected with a static time-division multiplexing network. By using the FiC-MPI simulator, parallel programs can be debugged before implementing on real machines. As a case study, the Himeno-BMT benchmark was implemented with FiC-MPI. It achieved 178.7 MFLOPS with a single node and scaled to 643.7 MFLOPS with four nodes, and 896.9 MFLOPS with six nodes of the M-KUBOS cluster. Through the implementation, the easiness of developing parallel programs with FiC-MPI on multi-FPGA systems was demonstrated. © 2022 IEEE.

关键词： parallel programming

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parallel Swarm Propagation for Neural Networks

Parallel Swarm Propagation for Neural Networks

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parallel Computing and distributed systems (PCDS), international symposium on

作者： Arghya Bhattacharya V Premanand School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu India

ISBN: (数字)9798350349658

ISBN: (纸本)9798350349665

Deep learning has revolutionized various fields but faces challenges such as limitations owing to vanishing gradients and the inability to handle non-differentiable loss functions and layers. Our study proposes parallel Swarm Propagation for Neural Networks, a novel approach that leverages parallel particle swarm optimization (PSO) for optimizing neural networks. PSO does not require gradient calculations and effectively circumvents the vanishing gradient problem. This enables the exploration of non-differentiable loss functions and layers, potentially leading to more robust and flexible deep-learning models. Additionally, the inherent parallelism of PSO allows for significant performance gains by utilizing the ever-increasing processing power of parallel-computing architectures. Our study investigates the efficacy of parallel PSO in optimizing neural networks, analyzing its performance compared to traditional gradient-based methods, and exploring its potential for unlocking new possibilities in deep learning.

关键词： Backpropagation Training Deep learning Scalability Neural networks Graphics processing units parallel processing Network architecture Hardware Particle swarm optimization

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Ensemble-Based System Benchmarking for HPC

Ensemble-Based System Benchmarking for HPC

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international symposium on parallel and distributed Computing

作者： Anna Fuchs Jannek Squar Michael Kuhn Universität Hamburg Hamburg Germany Otto von Guericke University of Magdeburg Magdeburg Germany

ISBN: (数字)9798350369199

ISBN: (纸本)9798350369205

In HPC supercomputers, the CPU, memory, network and storage play a critical role in application performance. Established benchmarks measure the theoretical peak performance of these components, but storage benchmarks often focus solely on I/O and lack realism. To address this, we present numio, a benchmark that simulates and evaluates overlapping compute, communication and I/O phases. Furthermore, we present a novel ensemble-driven system benchmarking strategy. This approach involves running multiple benchmarks in parallel to analyse their interactions and assess the system’s ability to handle the workload. Using a real-world example, we demonstrate how this approach reveals performance issues in complex HPC systems that remain hidden when using traditional methods using isolated benchmarks on empty systems.

关键词： Economics Data centers Runtime File systems System performance Estimation Benchmark testing Supercomputers Software distributed computing

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PIGO: A parallel Graph Input/Output Library

PIGO: A Parallel Graph Input/Output Library

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35th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Gabert, Kasimir Catalyurek, Umit, V Georgia Inst Technol Sch Computat Sci & Engn Atlanta GA 30332 USA

ISBN: (纸本)9781665435772

Graph and sparse matrix systems are highly tuned, able to run complex graph analytics in fractions of seconds on billion-edge graphs. For both developers and researchers, the focus has been on computational kernels and not end-to-end runtime. Despite the significant improvements that modern hardware and operating systems have made towards input and output, these can still become application bottlenecks. Unfortunately, on high-performance shared-memory graph systems running billion-scale graphs, reading the graph from file systems easily takes over 2000x longer than running the computational kernel. This slowdown causes both a disconnect for end users and a loss of productivity for researchers and developers. We close the gap by providing a simple to use, small, header-only, and dependency-free C++11 library that brings I/O improvements to graph and matrix systems. Using our library, we improve the end-to-end performance for state-of-the-art systems significantly-in many cases by over 40x.

关键词： Productivity distributed processing Runtime File systems Conferences Loading Libraries

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