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Construction of Hierarchical Matrix on distributed Memory Systems using a Task parallel Language

Construction of Hierarchical Matrix on Distributed Memory Sy...

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international symposium on computing and Networking Workshops (CANDARW)

作者： Zhengyang Bai Tasuku Hiraishi Akihiro Ida Masahiro Yasugi Keiichiro Fukazawa Gradudate School of Informatics Kyoto University Kyoto Japan Department of Information and Computer Science Kyoto Tachibana University Kyoto Japan Research Institute for Value-Added-Information Generation Japan Agency for Marine-Earth Science and Technology Yokohama Japan Department of Computer Science and Networks Kyushu Institute of Technology Iizuka Japan Academic Center for Computing and Media Studies Kyoto University Kyoto Japan

A hierarchical matrix is an approximated form of the dense matrix that represents the N-by-N correlations of N objects. The construction of the hierarchical matrix is achieved by dividing a matrix into submatrices (partitioning), followed by calculating these submatrices' entries (filling). The matrix partitioning is divided into two sub-steps: cluster tree (CT) construction by dividing objects into clusters, hierarchically, and block cluster tree (BCT) construction by observing all cluster pairs at the same level of the cluster tree that satisfy a given admissibility condition. We can apply task parallelism to filling by treating each submatrix as a parallelization unit. In existing implementations, the partitioning is redundantly executed on every computing node, and filling is executed in parallel by assigning a set of tasks to each worker statically. However, we cannot get good load balance using such implementations because it is difficult to predict the workload of each task precisely. In this paper, we propose a new parallel implementation of the hierarchical matrix construction, where a BCT construction is executed in parallel using all workers of all computing nodes. When a worker finds a BCT leaf during BCT construction, it executes filling for the submatrix corresponding to the leaf. To get good load balance, we parallelized the BCT construction using the task parallel language Tascell, which enables easy and efficient parallelization of tree recursive algorithms by employing a dynamic load balancing strategy. In numerical experiments with 3D electric field analyses using up to 16 computing nodes each of which has 36 cores, our implementation achieved up to 1.9-fold speedups compared to an implementation using the static task assignment strategy.

关键词： parallel languages Three-dimensional displays Correlation Heuristic algorithms Conferences parallel processing Load management

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HotQCD on Multi-GPU Systems 38

HotQCD on Multi-GPU Systems

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38th international symposium on Lattice Field Theory, LATTICE 2021

作者： Altenkort, L. Bollweg, D. Clarke, D.A. Kaczmarek, O. Mazur, L. Schmidt, C. Scior, P. Shu, Hai-Tao Fakultät für Physik Universität Bielefeld Bielefeld Germany Paderborn Center for Parallel Computing Paderborn University Paderborn Germany Physics Department Brookhaven National Laboratory UptonNY United States Institut für Theoretische Physik Universität Regensburg Regensburg Germany

We present SIMULATeQCD, HotQCD's software for performing lattice QCD calculations on GPUs. Started in late 2017 and intended as a full replacement of the previous single GPU lattice QCD code used by the HotQCD collaboration, our software has been developed into an extensive framework for lattice QCD calculations distributed on multiple GPUs over many compute nodes. The code is built on C++, CUDA, and MPI and leverages modern C++ language features to provide high-level data structures, objects, and algorithms that allow users to express lattice QCD calculations in an intuitive way without sacrificing performance. Implemented algorithms range from gradient flow, correlator measurements, and mixed precision conjugate gradient solvers all the way to full HISQ gauge field configuration generation using RHMC. After successful deployment in large-scale computing projects, we want to share the result of our efforts with the lattice QCD community by making it publicly available. In these proceedings, we will present some of the key features of our code, demonstrate its ease of use, and show benchmarks of performance critical kernels on state-of-the-art supercomputers. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 international License (CC BY-NC-ND 4.0)

关键词： Supercomputers

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DF-GAS: a distributed FPGA-as-a-Service Architecture towards Billion-Scale Graph-based Approximate Nearest Neighbor Search

DF-GAS: a Distributed FPGA-as-a-Service Architecture towards...

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IEEE/ACM international symposium on Microarchitecture (MICRO)

作者： Shulin Zeng Zhenhua Zhu Jun Liu Haoyu Zhang Guohao Dai Zixuan Zhou Shuangchen Li Xuefei Ning Yuan Xie Huazhong Yang Yu Wang Department of Electronic Engineering Tsinghua University Beijing China Qingyuan Research Institute Shanghai Jiao Tong University Shanghai China DAMO Academy Alibaba Group Sunnyvale USA ACCESS HKUST Hongkong China DAMO Academy Alibaba Group Sunnyvale USA

Embedding retrieval is a crucial task for recommendation systems. Graph-based approximate nearest neighbor search (GANNS) is the most commonly used method for retrieval, and achieves the best performance on billion-scale datasets. Unfortunately, the existing CPU- and GPU-based GANNS systems are difficult to optimize the throughput under the latency constraints on billion-scale datasets, due to the underutilized local memory bandwidth (5-45%) and the expensive remote data access overhead (∼85% of the total latency). In this paper, we first introduce a practically ideal GANNS architecture for billion-scale datasets, which facilitates a detailed analysis of the challenges and characteristics of distributed GANNS systems. Then, at the architecture level, we propose DF-GAS, a distributed FPGA-as-a-Service (FPaaS) architecture for accelerating billion-scale Graph-based Approximate nearest neighbor Search. DF-GAS uses a feature-packing memory access engine and a data prefetching and delayed processing scheme to increase local memory bandwidth by 36-42% and reduce remote data access overhead by 76.2%, respectively. At the system level, we exploit the "full-graph + sub-graph" hybrid parallel search scheme on distributed FPaaS system. It achieves million-level query-per-second with sub-millisecond latency on billion-scale GANNS for the first time. Extensive evaluations on million-scale and billion-scale datasets show that DF-GAS achieves an average of 55.4×, 32.2×, 5.4×, and 4.4× better latency-bounded throughput than CPUs, GPUs, and two state-of-the-art ANNS architectures, i.e., ANNA [23] and Vstore [27], *** CONCEPTS• Computer systems organization → Cloud computing.

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APDCM 2022 Keynote Talk: Solving QUBOs on Digital and Quantum Computers

APDCM 2022 Keynote Talk: Solving QUBOs on Digital and Quantu...

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

作者： Thorsten Koch Daniel Rehfeldt Yuji Shinano ZIB

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

Combinatorial Optimization searches for an optimum object in a finite but usually vast collection of objects. This can be used for many practical purposes, like efficient allocation of limited resources, network planning, and hundreds of other applications in almost all fields, e.g., finance, production, scheduling, and inventory control. However, many combinatorial optimization problems are known to be NP-hard, which is often translated simplistically as “intractable.” It is regularly claimed that quantum computers will bring breakthrough progress in solving such challenging combinatorial optimization problems relevant in practice. In particular, Quadratic Unconstraint Binary Optimization (QUBO) problems are said to be the model of choice for use in (adiabatic) quantum systems. We explain some of the meaning and implications, review the state of affairs, and give some computational results to underpin our conclusions.

关键词： Optimization Quantum computing distributed processing Conferences Computers Search problems Resource management

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Tangle Ledger for Decentralized Learning 34

Tangle Ledger for Decentralized Learning

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34th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Schmid, Robert Pfitzner, Bjarne Beilharz, Jossekin Arnrich, Bert Polze, Andreas Univ Potsdam Hasso Plattner Inst Potsdam Germany

ISBN: (纸本)9781728174457

Federated learning has the potential to make machine learning applicable to highly privacy-sensitive domains and distributed datasets. In some scenarios, however, a central server for aggregating the partial learning results is not available. In fully decentralized learning, a network of peer-to-peer nodes collaborates to form a consensus on a global model without a trusted aggregating party. Often, the network consists of Internet of Things (IoT) and Edge computing nodes. Previous approaches for decentralized learning map the gradient batching and averaging algorithm from traditional federated learning to blockchain architectures. In an open network of participating nodes, the threat of adversarial nodes introducing poisoned models into the network increases compared to a federated learning scenario which is controlled by a single authority. Hence, the decentralized architecture must additionally include a machine learning-aware fault tolerance mechanism to address the increased attack surface. We propose a tangle architecture for decentralized learning, where the validity of model updates is checked as part of the basic consensus. We provide an experimental evaluation of the proposed architecture, showing that it performs well in both model convergence and model poisoning protection.

关键词： machine learning consensus protocol edge computing information security fault tolerance

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PHRYCTORIA: A Messaging System for Transprecision OpenCAPI-attached FPGA Accelerators 34

PHRYCTORIA: A Messaging System for Transprecision OpenCAPI-a...

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34th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Diamantopoulos, Dionysios Purandare, Mitra Ringlein, Burkhard Hagleitner, Christoph IBM Res Europe Saumerstr 4 CH-8803 Ruschlikon Switzerland

ISBN: (纸本)9781728174457

Messaging frameworks are prevalent in distributed software systems as they simplify the exchange of data between applications running on different platforms. Messages are serialized and deserialized to transform user data structures into binary data for sending across the network. In a similar spirit, modern heterogenous computing platorms typically containing FPGAs can also profit from such messaging frameworks. Efficient compact serialization of data types is needed to take full advantage of the high throughput communication links. Transprecision extensions of FPGA replace native data types with novel low-precision data types. Transprecision extensions are shown to result in less-compact serializations. In this paper, we present a messaging framework for transprecision hardware accelerators which supports messages expressed in Protobuf and automatically generates serialization and deserialization functions. Our messageing framework leverages the varint encoding of Protocol buffers for efficient serialization and deserialization of transprecision data types. Evaluation results show that it can increase the effective throughput of OpenCAPI by up to 7.4x for various transprecision data-types.

关键词： Transprecision computing FPGAs serialization OpenCAPI protobuf coherent accelerators

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Tri-Objective Workflow Scheduling and Optimization in Heterogeneous Cloud Environments 34

Tri-Objective Workflow Scheduling and Optimization in Hetero...

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34th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Alrammah, Huda Gu, Yi Liu, Zhifeng Middle Tennessee State Univ Dept Comp Sci Murfreesboro TN 37132 USA Jiangsu Univ Sch Comp Sci & Telecommun Engn Zhenjiang Jiangsu Peoples R China

ISBN: (纸本)9781728174457

Cloud computing has become the most popular distributed computing paradigm among others which delivers scalable resources for efficient execution of large-scale scientific workflows. However, the large number of user requests and the limited cloud resources have posed a significant challenge on resource allocation, scheduling/mapping, power consumption, monetary cost, and so on. Therefore, how to schedule and optimize workflow execution in a cloud environment has become the most critical factor in improving the overall performance. Moreover, Multi-objective Optimization Problems (MOPs) along with heterogeneous cloud environments have made resource utilization and workflow scheduling even more challenging. In this work, we propose a novel algorithm, named Multi-objective Optimization for Makespan, Cost and Energy (MOMCE), to efficiently assign tasks to cloud resources in order to reduce total execution time, monetary cost, and energy consumption of scientific workflows. The experimental results have demonstrated the optimization stability and robustness of MOMCE algorithm for achieving a better fitness value in comparison with other existing algorithms.

关键词： large-scale scientific workflows cloud computing optimization makespan cost energy efficiency

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Memory-Disaggregated In-Memory Object Store Framework for Big Data Applications

Memory-Disaggregated In-Memory Object Store Framework for Bi...

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

作者： Robin Abrahamse Á kos Hadnagy Zaid Al-Ars Accelerated Big Data Systems Delft University of Technology Delft The Netherlands

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

The concept of memory disaggregation has recently been gaining traction in research. With memory disaggregation, data center compute nodes can directly access memory on adjacent nodes and are therefore able to overcome local memory restrictions, introducing a new data management paradigm for distributed computing. This paper proposes and demonstrates a memory disaggregated in-memory object store framework for big data applications by leveraging the newly introduced Thymes-isFlow memory disaggregation system. The framework extends the functionality of the pre-existing Apache Arrow Plasma object store framework to distributed systems by enabling clients to easily and efficiently produce and consume data objects across multiple compute nodes. This allows big data applications to increasingly leverage parallel processing at reduced development costs. In addition, the paper includes latency and throughput measurements that indicate only a modest performance penalty is incurred for remote disaggregated memory access as opposed to local (~6.5 vs ~5.75 GiB/s). The results can be used to guide the design of future systems that leverage memory disaggregation as well as the newly presented framework. This work is open-source and publicly accessible at https://***/10.5281/zenodo.6368998.

关键词： Data centers Memory management distributed databases Programming parallel processing Big Data applications Throughput

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The AMPERE Project: A Model-driven development framework for highly parallel and EneRgy-Efficient computation supporting multi-criteria optimization 23

The AMPERE Project: A Model-driven development framework for...

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IEEE 23rd international symposium on Real-Time distributed computing (ISORC)

作者： Quinones, Eduardo Royuela, Sara Scordino, Claudio Gai, Paolo Pinho, Luis Miguel Nogueira, Luis Rollo, Jan Cucinotta, Tommaso Biondi, Alessandro Hamann, Arne Ziegenbein, Dirk Saoud, Hadi Soulat, Romain Forsberg, Bjoern Benini, Luca Mando, Gianluca Rucher, Luigi Barcelona Supercomp Ctr Barcelona Spain Evidence Srl Pisa Italy Inst Super Engn Porto Porto Portugal Sysgo Prague Czech Republic Scuola Super Sant Anna Pisa Italy Bosch Stuttgart Germany Thales Res & Technol Palaiseau France Swiss Fed Inst Technol Zurich Switzerland Thales Italy Florence Italy

ISBN: (纸本)9781728169583

The high-performance requirements needed to implement the most advanced functionalities of current and future Cyber-Physical Systems (CPSs) are challenging the development processes of CPSs. On one side, CPSs rely on model-driven engineering (MDE) to satisfy the non-functional constraints and to ensure a smooth and safe integration of new features. On the other side, the use of complex parallel and heterogeneous embedded processor architectures becomes mandatory to cope with the performance requirements. In this regard, parallel programming models, such as OpenMP or CUDA, are a fundamental brick to fully exploit the performance capabilities of these architectures. However, parallel programming models are not compatible with current MDE approaches, creating a gap between the MDE used to develop CPSs and the parallel programming models supported by novel and future embedded platforms. The AMPERE project will bridge this gap by implementing a novel software architecture for the development of advanced CPSs. To do so, the proposed software architecture will be capable of capturing the definition of the components and communications described in the MDE framework, together with the non-functional properties, and transform it into key parallel constructs present in current parallel models, which may require extensions. These features will allow for making an efficient use of underlying parallel and heterogeneous architectures, while ensuring compliance with non-functional requirements, including those on real-time performance of the system.

关键词： parallel programming models parallel and heterogeneous embedded processor architectures model-driven approaches safety-critical embedded systems

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Scaling Optimizations for Large-Scale distributed Data with Lightweight Coresets 34

Scaling Optimizations for Large-Scale Distributed Data with ...

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34th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Pinheiro, Daniel N. Xavier-de-Souza, Samuel Aloise, Daniel Univ Fed Rio Grande do Norte Grad Program Elect & Comp Engn Natal RN Brazil Univ Fed Rio Grande do Norte Dept Comp Engn & Automat Natal RN Brazil Polytech Montreal Dept Comp & Software Engn Montreal PQ Canada

ISBN: (纸本)9781728174457

Lightweight coresets are compact representations of data sets such that clustering methods present competitive results in relation to the complete data set. They are constructed by sampling important points from the complete set. We propose a fast method to approximate the sampling of lightweight coresets from very large data sets which are distributed among multiple machines. We show that the proposed method is much faster and scalable, reaching results 48 times faster than the original lightweight coresets, while holding similar properties.

关键词： lightweight coresets k-means clustering Big Data high-performance computing distributed data

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