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IEEE symposium on Bioinformatics and Bioengineering (BIBE)

作者： Roman Snytsar Research and AI Microsoft Redmond USA

ISBN: (纸本)9781728146188

Striped variation of the Smith-Waterman algorithm is known as extremely efficient and easily adaptable for the SIMD architectures. However, the potential for improvement has not been exhausted yet. The popular Lazy-F loop heuristic requires additional memory access operations, and the worst-case performance of the loop could be as bad as the nonvectorized version. We demonstrate the progression of the lazy-F loop transformations that improve the loop performance, and ultimately eliminate the loop completely. Our algorithm achieves the best asymptotic performance of all scan-based SW algorithms O(n/p+log(p)), and is very efficient in practice.

关键词： Bioinformatics parallel processing Optimization Arrays Computational complexity Instruction sets

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Enabling rapid development of parallel tree search applications

Proceedings of the 5th IEEE Workshop on Challenges of Large ...

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Proceedings of the 5th IEEE Workshop on Challenges of Large Applications in Distributed Environments, CLADE'07 2007年 1-10页

作者： Gardner, Jeffrey P. Connolly, Andrew McBride, Cameron Pittsburgh Supercomputing Center 300 S. Craig St. Pittsburgh PA 15213 University of Washington Department of Astronomy Seattle WA 98195 University of Pittsburgh Department of Physics and Astronomy 3941 O'Hara St. Pittsburgh PA 15260

ISBN: (纸本)1595937145

Virtual observatories will give astronomers easy access to anunprecedented amount of data. Extracting scientific knowledge from these data will increasingly demand both efficient algorithms as well as the power of parallel computers. Nearly all efficient analyses of large astronomical datasets use trees as their fundamental data structure. Writing efficient tree-based techniques, a task that is time-consuming even on single-processor computers, is exceedingly cumbersome on massively parallel platforms (MPPs). Most applications that run on MPPs are simulation codes, since the expense of developing them is offset by the fact that they will be used for many years by many researchers. In contrast, data analysis codes change far more rapidly, are often unique to individual researchers, and therefore accommodate little reuse. Consequently, the economics of the current high-performance computing development paradigm for MPPs does not favor data analysis applications. We have therefore built a library, called Ntropy, that provides a flexible, extensible, and easy-to-use way of developing tree-based data analysis algorithms for both serial and parallel platforms. Our experience has shown that not only does our library save development time, it can also deliver excellent serial performance and parallel scalability. Furthermore, Ntropy makes it easy for an astronomer with little or noparallel programming experience to quickly scale their application to a distributed multiprocessor environment. By minimizing development time for efficient and scalable data analysis, we enable wide-scale knowledge discovery on massive datasets. Copyright 2007 ACM.

关键词： Data reduction

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Real-Time Non-rigid Registration of Medical Images on a Cooperative parallel Architecture

Real-Time Non-rigid Registration of Medical Images on a Coop...

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IEEE international Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yixun Liu Andriy Fedorov Ron Kikinis Nikos Chrisochoides Department of Computer Science College of William and Mary Williamsburg USA Surgical Planning Laboratory Brigham and Women''s Hospital Boston USA

Unacceptable execution time of Non-rigid registration (NRR) often presents a major obstacle to its routine clinical use. parallel computing is an effective way to accelerate NRR. However, development of efficient parallel NRR codes is a very challenging task. One desirable approach is to map the existing sequential algorithm to the parallel architecture to gain speedup instead of designing a new parallel algorithm. Multicores and GPU provide us a cooperative architecture, in which both Single Instruction Multiple Data (SIMD) and Single Program Multiple Data (SPMD) programming models can co-exist and complement each other. We present a method to parallelize a NRR on this cooperative architecture. Our approach is first to separate the sequential algorithm into regular and irregular parts. We then map the regular part on GPU following SIMD paradigm and irregular part on multicores in a SPMD fashion. Unlike the approaches that use multicores or GPU alone, our approach leads to desirable speedup for the whole application by taking advantage of all components of the cooperative parallel architecture, for all individual parts of the application. This helps us to get closer to our goal: cheaper and faster NRR that leads to its more widespread use. The results on clinical brain MRI data show that the GPU-based Block Matching (regular part) can run at least 1.9 times faster than on a typical cluster of workstations with eight high-performance nodes. The multicores-based implementation of the incremental finite element solver (irregular part) achieves speedup of up to 7 times compared to its sequential version. As a result, the total run time of the NRR code can be reduced to less than 1 minute therefore satisfying the real time requirement for its clinical application.

关键词： Biomedical imaging parallel architectures Multicore processing parallel processing Acceleration Algorithm design and analysis parallel algorithms Magnetic resonance imaging Workstations Finite element methods

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Robust Server Placement for Edge Computing

Robust Server Placement for Edge Computing

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international symposium on parallel and Distributed Processing (IPDPS)

作者： Dongyu Lu Yuben Qu Fan Wu Haipeng Dai Chao Dong Guihai Chen Department of Computer Science and Engineering Shanghai Jiao Tong University China Department of Computer Science and Technology Nanjing University China College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics China

ISBN: (数字)9781728168760

ISBN: (纸本)9781728168777

In this work, we study the problem of Robust Server Placement (RSP) for edge computing, i.e., in the presence of uncertain edge server failures, how to determine a server placement strategy to maximize the expected overall workload that can be served by edge servers. We mathematically formulate the RSP problem in the form of robust max-min optimization, derived from two consequentially equivalent transformations of the problem that does not consider robustness and followed by a robust conversion. RSP is challenging to solve, because the explicit expression of the objective function in RSP is hard to obtain, and RSP is a robust max-min problem with a matroid constraint and a knapsack constraint, which is still an unexplored problem in the literature. To address the above challenges, we first investigate the special properties of the problem, and reveal that the objective function is monotone submodular. We then prove that the involved constraints form a p-independence system constraint, where p is a constant value related to the ratio of the coefficients in the knapsack constraint. Finally, we propose an algorithm that achieves a provable constant approximation ratio in polynomial time. Both synthetic and trace-driven simulation results show that, given any maximum number of server failures, our proposed algorithm outperforms three state-of-the-art algorithms and approaches the optimal solution, which applies exhaustive exponential searches.

关键词： Servers Robustness Optimization Approximation algorithms Linear programming Resource management Delays

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Integration of Modern HPC Performance Tools in Vlasiator for Exascale Analysis and Optimization

Integration of Modern HPC Performance Tools in Vlasiator for...

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

作者： Camille Coti Yann Pfau-Kempf Markus Battarbee Urs Ganse Sameer Shende Kevin Huck Jordi Rodriquez Leo Kotipalo Jennifer Faj Jeremy J. Williams Ivy Peng Allen D. Malony Stefano Markidis Minna Palmroth École de Technologie Supérieure University of Helsinki University of Oregon KTH Royal Institute of Technology Finnish Meteorological Institute

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

Key to the success of developing high-performance applications for present and future heterogeneous supercomputers will be the systematic use of measurement and analysis to understand factors that affect delivered performance in the context of parallelization strategy, heterogeneous programming methodology, data partitioning, and scalable algorithm design. The evolving complexity of future exascale platforms makes it unrealistic for application teams to implement their own tools. Similarly, it is naïve to expect available robust performance tools to work effectively out-of-the-box, without integration and specialization in respect to application-specific requirements and knowledge. Vlasiator is a powerful massively parallel code for accurate magnetospheric and solar wind plasma simulations. It is being ported to the LUMI HPC system for advanced modeling of the Earth's magnetosphere and surrounding solar wind. Building on a preexisting Vlasiator performance API called Phiprof, our work significantly advances the performance measurement and analysis capabilities offered to Vlasiator using the TAU, APEX, and IPM tools. The results presented show in-depth characterization of node-level CPU/GPU and MPI communications performance. We highlight the integration of high-level Phiprof events with detailed performance data to expose opportunities for performance tuning. Our results provide important insights to optimize Vlasiator for the upcoming Exascale machines.

关键词： Measurement Earth Plasma simulation Systematics programming Supercomputers Partitioning algorithms

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Parla: a Python orchestration system for heterogeneous architectures 22

Parla: a Python orchestration system for heterogeneous archi...

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Proceedings of the international Conference on High Performance Computing, Networking, Storage and Analysis

作者： Hochan Lee William Ruys Ian Henriksen Arthur Peters Yineng Yan Sean Stephens Bozhi You Henrique Fingler Martin Burtscher Milos Gligoric Karl Schulz Keshav Pingali Christopher J. Rossbach Mattan Erez George Biros The University of Texas at Austin Texas State University

Python's ease of use and rich collection of numeric libraries make it an excellent choice for rapidly developing scientific applications. However, composing these libraries to take advantage of complex heterogeneous nodes is still difficult. To simplify writing multi-device code, we created Parla, a heterogeneous task-based programming framework that fully supports Python's scientific programming stack. Parla's API is based on Python decorators and allows users to wrap code in Parla tasks for parallel execution. Parla arrays enable automatic movement of data between devices. The Parla runtime handles resource-aware mapping, scheduling, and execution of tasks. Compared to other Python tasking systems, Parla is unique in its parallelization of tasks within a single process, its GPU context and resource-aware runtime, and its design around gradual adoption to provide easy migration of and integration into existing Python applications. We show that Parla can achieve performance competitive with hand-optimized code while improving ease of development.

关键词： heterogeneous computing parallel application frameworks load balancing and scheduling algorithms task based parallelism

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Time-parallel computational strategy for FDTD solution of Maxwell's equations

Time-parallel computational strategy for FDTD solution of Ma...

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Antennas and Propagation Society international symposium

作者： M.A. Jensen A. Fijany Y. Rahmat-Samii Department of Elec Engineering UCLA Los Angeles CA USA JPL California Institute of Technology Pasadena CA USA

A major emphasis within the computational electromagnetics (CEM) community concerns the solution of Maxwell's differential equations using finite-difference time-domain (FDTD) techniques. Because of the computational time and memory requirements associated with these time-stepping algorithms, their application to very large problems has been somewhat limited. To alleviate these computational obstacles, some efforts have previously been aimed at the implementation of space-parallelism-the concurrent computation of unknowns at different points in the spatial mesh using multiple processors-in the FDTD algorithms. For these schemes, however, communication and synchronization requirements have limited the amount of computational speed-up provided by the use of additional processors. This limited potential for enhanced computational efficiency implies that if full exploitation of the capabilities of emerging multiple instruction multiple data (MIMD) architectures is to be realized, approaches must be developed which represent a drastic departure from traditional FDTD techniques. The aim of the paper is to present such a computational strategy. Unlike traditional approaches which use space-parallelism this methodology exploits the decoupling mechanism of an eigenvalue-eigenvector (EE) decomposition of the FDTD matrix to allow the efficient implementation of time-parallelism-the simultaneous computation of field values at multiple time steps. The resulting algorithm is highly coarse grain and has minimum communication and synchronization requirements.< >

关键词： Finite difference methods Time domain analysis Maxwell equations Matrix decomposition Concurrent computing Laplace equations Sparse matrices Differential equations Computational efficiency Computer architecture

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Performance analysis of a multithreaded pricing algorithm on Cilk

Performance analysis of a multithreaded pricing algorithm on...

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IEEE international symposium on High Performance Computing Systems and Applications (HPCS)

作者： R.K. Thulasiram P. Thulasiraman C. Adiele D. Bondarenko Department of Computer Science University of Manitoba Winnipeg Canada

In this paper, we develop a multithreaded algorithm for pricing simple options and implement it on a 8 node SMP machine using MIT's supercomputer programming language Cilk. The algorithm dynamically creates lots of threads to exploit parallelism and relies on the Cilk runtime system to distribute the computation load. We present both analytical and experimental results and our results explain how Cilk could be used effectively to exploit parallelism in the given problem. The analytical results show that our algorithm has a very high average parallelism and hence Cilk is the target paradigm to implement the algorithm. We conclude from our implementation results that the size of the threads, the number of threads created, the load balancer the cost of spawning a thread are parameters that must be considered while designing the algorithm on the Cilk platform.

关键词： Performance analysis Pricing Yarn parallel processing Algorithm design and analysis Supercomputers Computer languages Heuristic algorithms Concurrent computing Distributed computing

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parallel Simulation of Large-Scale Universal Particle Systems Using CUDA

Parallel Simulation of Large-Scale Universal Particle System...

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IEEE international symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Xiangfei Li Xuzhi Wang Wanggen Wan Xiaoqiang Zhu Xiaoqing Yu Institute of Smart City Shanghai University Shanghai China

Particle systems' greatest advantage is well suited for modeling complex fuzzy phenomena, such as explosions, fountain, tornado and fireworks, etc. in 3D graphics. With the increasing requirements on the number of particles and particle-particle interactions, the computational complexity of simulation in particle systems has increased rapidly. Particle systems are traditionally implemented on a general-purpose CPU, and the computational complexity of particle systems limits the number of particles that can be computed at interactive rates. This paper focuses on real-time simulation of large-scale particle systems. We discuss optional integration algorithms based on CUDA (Compute Unified Device Architecture) for both graphic and scientific simulation. The speed of particle systems has been greatly improved, with parallel-core GPUs working in tandem with multi-core CPUs. In order to provide a scalable and portable API library, the object-oriented programming method is adopted to encapsulate the functions of parallel particle system. Results show that our proposed APIs are user-friendly and the parallel implementations are significantly efficient.

关键词： Graphics processing units Libraries Computational modeling Vectors Object oriented modeling Mathematical model Algorithm design and analysis

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SeqHive: A Reconfigurable Computer Cluster for Genome Re-sequencing

SeqHive: A Reconfigurable Computer Cluster for Genome Re-seq...

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international Conference on Field Programmable Logic and Applications

作者： Kristian Stevens Henry Chen Terry Filiba Peter McMahon Yun S. Song Department of Computer Science University of California Davis Davis CA USA Berkeley Wireless Research Center Berkeley CA USA Department of Electrical Engineering University of Stanford Stanford CA USA Department of Electrical Engineering and Computer Sciences University of California Berkeley Berkeley CA USA

We demonstrate how Field Programmable Gate Arrays (FPGAs) may be used to address the computing challenges associated with assembling genome sequences from recent ultra-high-throughput sequencing technologies. Advances in sequencing technology allow researchers to generate immense amounts of raw data in the form of short reads with high error rates. A prerequisite to effectively utilizing this data for most applications is accurate alignment to a reference genome. While dynamic programming (DP) alignment algorithms are generally avoided on conventional architectures due to their computational complexity, they can be tailored for efficient implementation on systolic architectures. We describe and implement the first system capable of assembling large genomes using DP. We implemented application-specific DP algorithms for aligning data from ultra-high-throughput sequencers in a reconfigurable computing cluster. To obtain the necessary throughput while maintaining scoring integrity, we extended the compact encoding scheme of Lipton and Lopresti for our application. Each FPGA is capable of rapidly aligning multiple reads in parallel against a long reference genome. The reconfigurable cluster proves to be scalable and capable of processing real world datasets with a sustained performance of 11 tera cell updates per second. We examine the advantages and practicality of our system by benchmarking real genomic data from a large sequencing project. Our exhaustive validation confirms that application specific computing hardware can provide more accurate results than current heuristic methods and remain practical. While directly addressing the important problem of genomic assembly, particularly in circumstances where error rates or evolutionary divergence is high, the methods presented are also relevant to many other current applications for this type of data.

关键词： Field programmable gate arrays Genomics Bioinformatics Computer architecture Random access memory Encoding parallel processing

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