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HTS: A Threaded Multilevel Sparse Hybrid Solver

HTS: A Threaded Multilevel Sparse Hybrid Solver

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international symposium on parallel and distributed Processing (IPDPS)

作者： Joshua Dennis Booth University of Alabama in Huntsville Huntsville AL USA

Large shared-memory many-core nodes have become the norm in scientific computing, and therefore the sparse linear solver stack must adapt to the multilevel structure that exists on these nodes. One adaption is the development of hybrid-solvers at the node level. We present HTS as a hybrid threaded solver that aims to provide a finer-grain algorithm to keep an increased number of threads actively working on these larger shared-memory environments without the overheads of message passing implementations. Additionally, HTS aims at utilizing the additional shared memory that may be available to improve performance, i.e., reducing iteration counts when used as a preconditioner and speeding up calculations. HTS is built around the Schur complement framework that many other hybrid solver packages already use. However, HTS uses a multilevel structure in dealing with the Schur complement and allows for fill-in in certain off-diagonal submatrices to allow for a faster and more accurate solve phase. These modifications allow for a tasking thread library, namely Cilk, to be used to speed up performance while still reducing peak memory by more than 20% on average compared to an optimized direct factorization method. We show that HTS can outperform the MPI-based hybrid solver ShyLU on a suite of sparse matrices by as much as 2×, and show that HTS can scale well on three-dimensional finite difference problems.

关键词： High-temperature superconductors Scientific computing Scalability Particle separators parallel processing Libraries Sparse matrices

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Hybrid Workflow Provisioning and Scheduling on Edge Cloud computing Using a Gradient Descent Search Approach 19

Hybrid Workflow Provisioning and Scheduling on Edge Cloud Co...

引用

19th international symposium on parallel and distributed computing (ISPDC)

作者： Alsurdeh, Raed Calheiros, Rodrigo N. Matawie, Kenan M. Javadi, Bahman Western Sydney Univ Sch Comp Data & Math Sci Sydney NSW Australia

ISBN: (纸本)9781728189468

The dramatic growth of the Internet of Things (IoT) technology in many application domains, ranging from intelligent video surveillance, smart retail to the Internet-of-Vehicles brings new computation challenges for rationalized utilization of computing resources. IoT application execution refers to hybrid processing model of stream and batch to achieve data analytics objectives. Hybrid workflow execution combines the challenges of latency-sensitive and resource-intensive processing. To resolve these challenges, we proposed a two stages hybrid workflow scheduling framework on edge cloud computing. In the first stage, we proposed a resource estimation algorithm based on a linear optimization approach, the gradient descent search (GDS) and in the second stage, we adopted a cluster-based provisioning and scheduling technique on heterogeneous edge cloud resources. This work provides a multi-objective optimization model for execution time and monetary cost under constraints of deadline and throughput. Results demonstrated the framework performance in controlling the execution of hybrid workflows by an efficient tuning for stream processing parameters, such as arrival rate and processing throughput. Under working constraints, the proposed scheduler provides significant improvement for large hybrid workflows in terms of execution time and monetary cost with an average of 8% and 35%, respectively.

关键词： Multiobjective optimization

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Accelerated implementation of FQSqueezer novel genomic compression method 19

Accelerated implementation of FQSqueezer novel genomic compr...

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19th international symposium on parallel and distributed computing (ISPDC)

作者： Amich, Monica De Luca, Pasquale Fiscale, Stefano Univ Campania Luigi Vanvitelli DiSTABiF Caserta Italy Univ Salerno Dept Comp Sci Fisciano Italy Univ Naples Parthenope Dept Sci & Technol Naples Italy

ISBN: (纸本)9781728189468

Biological data contain very important information for genoma analysis. In last decades, the size of these data is constantly growing. So the Next Generation Sequence (NGS) data has been introduced. These kind of data are represented by different data formats, such as FASTQ, FASTA, SAM, etc. In order to allow a good analysis and storing of them, due to large dimension of these data, several compressors have been performed. FQSqueezer is a novel genomic compressor for FASTQ data files. But several issues are present due to multithread version that runs on multi-core hardware. It is well-known that the number of cores in a CPU is limited and very minor with respect to GPUs' cores number. In order to increase the performance related to this compressor method, in this work we present a GPU-parallel implementation of cited compressor by exploiting CUDA framework. More precisely, a suitable domain decomposition is able to give an appreciable gain of performance in terms of time and reliability. Several execution tests confirm the gain of efficiency achieved by our parallel implementation.

关键词： Next Generation Sequence parallel algorithm GPU genomic compression FASTQ

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APC-SCA: A Fully-parallel Annealing Algorithm with Autonomous Pinning Effect Control

APC-SCA: A Fully-Parallel Annealing Algorithm with Autonomou...

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

作者： Daiki Okonogi Satoru Jimbo Kota Ando Thiem Van Chu Jaehoon Yu Masato Motomura Kazushi Kawamura AI Computing Research Unit Tokyo Institute of Technology Yokohama Japan

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

Annealing computation has recently attracted attention as it can efficiently solve various combinatorial optimization problems using an Ising model. Stochastic cellular automata annealing (SCA) is a promising algorithm that can realize fast spin-update by utilizing its parallel computing capability. However, in SCA, preparing an appropriate control of the pinning parameter is a hard task, which degrades its usability. This paper proposes a novel approach called APC-SCA (Autonomous Pinning effect Control SCA) where the spin pinning parameter can be controlled autonomously by observing individual spin flips. The evaluation results using max-cut and N-queen problems demonstrate that the proposed approach can obtain better solutions than the conventional approach with a grid search of optimal pinning parameter control.

关键词： Annealing Stationary state Stochastic processes Graphics processing units parallel processing Search problems Hardware

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A Hybrid Solution to Provide End-to-End Flow Control and Congestion Management in High-Performance Interconnection Networks

A Hybrid Solution to Provide End-to-End Flow Control and Con...

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IEEE/ACM international symposium on Cluster computing and the Grid (CCGRID)

作者： Alberto Merino Jesus Escudero-Sahuquillo Pedro Javier Garcia Francisco J. Quiles Fei Chen Yunping Lyu Long Yan Jose Duato Computing Systems Department Universidad de Castilla-La Mancha Spain Huawei Technologies Co. Ltd. Shenzhen China Royal Spanish Academy of Sciences Spain

ISBN: (数字)9798350395662

ISBN: (纸本)9798350395679

Congestion seriously threatens high-performance interconnection networks in supercomputers and data centers, where thousands of server nodes generate massive communication operations when running highly parallel and distributed applications and services. In recent years, numerous solutions have been proposed to address congestion and its effects, including flow control (e.g., priority flow control, PFC) to prevent packet dropping at congested buffers, injection throttling to detect congested points and notify source server nodes to reduce the injection rate of congesting flows, and congestion isolation (as defined in the IEEE 802.1Qcz standard) that stores the congesting flows in separate queues or virtual channels (VCs) at switch buffers. Unfortunately, these solutions have exhibited important drawbacks, such as the prohibitive latency generated by flow control during congestion situations, the slow and ineffective response of injection throttling, or the excessive resources required to identify and isolate congesting flows. In this paper, we propose a hybrid congestion management solution, called 3SC (from three strategies combined), which combines end-to-end flow control, injection throttling, and congesting-flow isolation. 3SC significantly reduces Head-of-Line (HoL) blocking by isolating congesting flows in special queues at some switches, swiftly throttles the injection of these congesting flows, and significantly decreases the number of flow control messages compared to PFC. To evaluate our proposal, we have conducted a large set of simulation experiments for different network configurations and realistic traffic patterns. The results demonstrate that 3SC is efficient and feasible, making it a promising solution for future interconnection network designs.

关键词： Degradation Data centers Cloud computing Multiprocessor interconnection Switches Traffic control Supercomputers Servers Proposals Standards

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Optimal Matrix Partitioning for Data parallel computing on Hybrid Heterogeneous Platforms 19

Optimal Matrix Partitioning for Data Parallel Computing on H...

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19th international symposium on parallel and distributed computing (ISPDC)

作者： Malik, Tania Lastovetsky, Alexey Sch Comp Sci UCD Dublin Ireland

ISBN: (纸本)9781728189468

In this paper, we study the problem of partitioning a matrix over a small number of interconnected heterogeneous processors. This problem is crucial for data parallel dense linear algebra and other applications with similar communication patterns on modern hybrid servers, integrating several heterogeneous compute devices such as CPUs, GPUs and other accelerators. The objective is to balance the load of the heterogeneous devices while minimising the communication cost. While the problem has been solved for the case of two processors, it is still open for three and more processors. The state-of-the-art solution for the case of three processors uses a communication cost function, which does not accurately account for the total amount of data moved between processors and therefore leaves the question of its global optimality open. In this work, we propose a cost function, which accurately represents the total amount of data moved between processors. Then, we formulate and solve the problem of optimal partitioning of a square computational domain, using this accurate communication cost function. Finally, we propose and implement an original experimental methodology for accurate measurement of the communication time of parallel applications on hybrid heterogeneous servers, integrating multi-core CPUs and various accelerators. We apply this methodology to experimental validation of our mathematical result.

关键词： Matrix multiplication data partitioning hybrid platforms heterogeneous platforms non-rectangular partitioning optimal partitioning communication optimization data parallelism

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Efficient parallel Shortest Path Algorithms 19

Efficient Parallel Shortest Path Algorithms

引用

19th international symposium on parallel and distributed computing (ISPDC)

作者： Alves, David R. Krishnakumar, Madan S. Garg, Vijay K. Univ Texas Austin Dept Elect & Comp Engn Austin TX 78712 USA

ISBN: (纸本)9781728189468

Finding the shortest path between nodes in a graph has wide applications in many important areas such as transportation and computer networks. However, the current reference algorithms for this task, Dijkstra's for single threaded environments and Delta-stepping for multi-threaded ones, leave performance and efficiency on the table by not taking advantage of additional information available about the graph. In this paper we present and experimentally evaluate novel algorithms SP1, SP2 and ParSP(2) that leverage these constraints to solve the problem faster and more efficiently in key metrics. In single threaded execution, we show how SP1 and SP2 out-perform Dijsktra's algorithm by up to 46%. In multi-threaded execution we show how our algorithms compare favorably to Delta-stepping algorithm in the ability to establish the shortest path between the source and the median node.

关键词： Single Source Shortest Path Problem Dijkstra's Algorithm

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Peachy parallel Assignments (EduPar 2022)

Peachy Parallel Assignments (EduPar 2022)

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

作者： H. Martin Bü cker Henri Casanova Rafael Ferreira da Silva Alice Lasserre Derrick Luyen Raymond Namyst Johannes Schoder Pierre-André Wacrenier David P. Bunde Institute for Computer Science Friedrich Schiller University Jena Jena Germany Information and Computer Sciences University of Hawaii Honolulu HI USA National Center for Computational Sciences Oak Ridge National Laboratory Oak Ridge TN USA Computer Science Department University of Bordeaux Inria Bordeaux Sud-Ouest Talence France Computer Science Department Knox College Galesburg IL USA

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

The presentation of Peachy parallel Assign-ments in several workshops on parallel and distributed computing education aims to promote the reuse of high-quality assignments, both saving precious faculty time and improving the quality of course assignments. Presented assignments are selected competitively- they must have been successfully used in a real classroom, be easy for other instructors to adopt, and be “cool and inspirational” to encourage students to spend time on them and talk about them with others. Winning assignments are also archived on the Peachy parallel Assignments website. In this installment of Peachy parallel Assignments, we present three new assignments. The first assignment is to simulate an Abelian Sandpile, with grains of sand moving from tall piles to shorter ones. This is a discrete simulation that creates colorful and intricate images. The second assignment is a Big Data problem in which students use the MapReduce paradigm to recreate “Warming Stripes”, a visualization of climate data that highlights climate change. The third assignment introduces climate-oriented optimization by asking students to schedule distributed workflows to minimize their carbon footprint.

关键词： Schedules distributed processing Climate change Conferences Education Data visualization distributed computing

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MigHEFT: DAG-based Scheduling of Migratable Tasks on Heterogeneous Compute Nodes 34

MigHEFT: DAG-based Scheduling of Migratable Tasks on Heterog...

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

作者： Loesch, Achim Platzner, Marco Paderborn Univ Comp Engn Grp Paderborn Germany

ISBN: (数字)9781728174457

ISBN: (纸本)9781728174457

Application tasks expose different computational demands that can be best served by different architectures such as CPUs, GPU, and FPGAs. Existent execution models do not allow for migrating tasks across different computing resources at runtime, which limits scheduling flexibility. In this paper we first describe our execution model and runtime system that enable heterogeneous migration for data-parallel applications. Then, we focus on the problem of scheduling a set of dependent tasks on a heterogeneous compute node by exploiting this novel feature of heterogenous task migration. We present the MigHEFT scheduling approach that combines an off-line created schedule based on estimations of task execution times with runtime monitoring and rescheduling, possibly leading to migration. We evaluate MigHEFT on a broad range of DAGs exhibiting different runtime characteristics and compare it with HEFT algorithms, widely-used reference schedulers for minimizing the makespan for a set of dependent tasks on heterogeneous resources. We demonstrate that due to the novel migration capability, our scheduling approach achieves an average speedup of 14.1% over HEFT and 8.6% over HEFT extended with rescheduling.

关键词： Heterogeneous computing High-Performance computing Task Scheduling Task Migration Task Dependencies

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Traffic-Optimal Virtual Network Function Placement and Migration in Dynamic Cloud Data Centers

Traffic-Optimal Virtual Network Function Placement and Migra...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Vincent Tran Jingsong Sun Bin Tang Deng Pan Department of Computer Science California State University Dominguez Hills School of Computing and Information Sciences Florida International University

We propose a new algorithmic framework for traffic-optimal virtual network function (VNF) placement and migration for policy-preserving data centers (PPDCs). As dynamic virtual machine (VM) traffic must traverse a sequence of VNFs in PPDCs, it generates more network traffic, consumes higher bandwidth, and causes additional traffic delays than a traditional data center. We design optimal, approximation, and heuristic traffic-aware VNF placement and migration algorithms to minimize the total network traffic in the PPDC. In particular, we propose the first traffic-aware constant-factor approximation algorithm for VNF placement, a Pareto-optimal solution for VNF migration, and a suite of efficient dynamic-programming (DP)-based heuristics that further improves the approximation solution. At the core of our framework are two new graph-theoretical problems that have not been studied. Using flow characteristics found in production data centers and realistic traffic patterns, we show that a) our VNF migration techniques are effective in mitigating dynamic traffic in PPDCs, reducing the total traffic cost by up to 73%, b) our VNF placement algorithms yield traffic costs 56% to 64% smaller than those by existing techniques, and c) our VNF migration algorithms outperform the state-of-the-art VM migration algorithms by up to 63% in reducing dynamic network traffic.

关键词： Data centers Cloud computing distributed processing Costs Heuristic algorithms Telecommunication traffic Production

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