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A heterogeneity-aware approach to load balancing of computational tasks: a theoretical and simulation study

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2008年第2期11卷 133-149页

作者： Huang, Jun Lee, Soo-Young Auburn Univ Dept Elect & Comp Engn Auburn AL 36849 USA

One of the distinct characteristics of computing platforms shared by multiple users such as a cluster and a computational grid is heterogeneity on each computer and/or among computers. Temporal heterogeneity refers to variation, along the time dimension, of computing power available for a task on a computer, and spatial heterogeneity represents the variation among computers. In minimizing the average parallel execution time of a target task on a spatially heterogeneous computing system, it is not optimal to distribute the target task linearly proportional to the average computing powers available on computers. In this paper, effects of the temporal and spatial heterogeneity on performance of a target task have been analyzed in terms of the mean and standard deviation of parallel execution time. Based on the analysis results, an approach to load balancing for minimizing the average parallel execution time of a target task is described. The proposed approach whose validity has been verified through simulation considers temporal and spatial heterogeneities in addition to the average computing power on each computer.

关键词： average execution time load balancing random variables spatial heterogeneity stochastic model temporal heterogeneity

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Flexible Restart Mechanism for Successive Cancellation Flip Decoding of Polar Codes

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IEEE COMMUNICATIONS LETTERS 2024年第11期28卷 2459-2463页

作者： Xue, Xiyue He, Meilin Rui, Guo Hangzhou Dianzi Univ Sch Commun Engn Hangzhou 310018 Peoples R China

Polar codes can achieve near-capacity performance under low-complexity successive cancellation (SC) decoding. However, short to medium-length block codes exhibit insufficient error-correction performance with the SC algorithm, which limits their practical applications. Although the successive cancellation flip (SCF) decoder improves SC performance, its variable execution time poses challenges in certain scenarios. The simplified restart mechanism (SRM) reduces the execution time of SCF decoders through conditional restart of the additional trials from the second half of the codeword. However, regardless of the position of the flipped bit on the RHS, additional decoding trials can only start from a fixed restart position, which limits efficiency improvements. This letter proposes a flexible restart mechanism (FRM), introducing multiple optional restart positions to further optimize the simplified restart mechanism, reducing both execution time and its variability. In simulations, we used P(1024, 128) and P(2048, 256) polar codes to compare the performance of SCF decoders embedding with FRM and SRM. The results indicate that, compared to the SCF decoder, the SCF decoder with FRM reduces average execution time, average additional execution time, execution time variance, and median additional execution time by 14.95%, 63.42%, 86.55%, and 58.20%, respectively. Compared to the SCF decoder embedding with SRM, FRM achieves improvements in these metrics by 5.15%, 23.14%, 20.24%, and 29.71%, respectively.

关键词： Decoding Polar codes Codes Indexes Heuristic algorithms Vectors Standards Flexible restart mechanism (FRM) SCF SRM average execution time restart positions reduction

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Autonomic care platform for optimizing query performance

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BMC MEDICAL INFORMATICS AND DECISION MAKING 2013年第1期13卷 1-18页

作者： Steurbaut, Kristof Latre, Steven Decruyenaere, Johan De Turck, Filip Univ Ghent Dept Informat Technol IMinds Gaston B-9050 Ghent Belgium Univ Antwerp Dept Math & Comp Sci IMinds B-2020 Antwerp Belgium Ghent Univ Hosp Dept Intens Care B-9000 Ghent Belgium

Background: As the amount of information in electronic health care systems increases, data operations get more complicated and time-consuming. Intensive Care platforms require a timely processing of data retrievals to guarantee the continuous display of recent data of patients. Physicians and nurses rely on this data for their decision making. Manual optimization of query executions has become difficult to handle due to the increased amount of queries across multiple sources. Hence, a more automated management is necessary to increase the performance of database queries. The autonomic computing paradigm promises an approach in which the system adapts itself and acts as self-managing entity, thereby limiting human interventions and taking actions. Despite the usage of autonomic control loops in network and software systems, this approach has not been applied so far for health information systems. Methods: We extend the COSARA architecture, an infection surveillance and antibiotic management service platform for the Intensive Care Unit (ICU), with self-managed components to increase the performance of data retrievals. We used real-life ICU COSARA queries to analyse slow performance and measure the impact of optimizations. Each day more than 2 million COSARA queries are executed. Three control loops, which monitor the executions and take action, have been proposed: reactive, deliberative and reflective control loops. We focus on improvements of the execution time of microbiology queries directly related to the visual displays of patients' data on the bedside screens. Results: The results show that autonomic control loops are beneficial for the optimizations in the data executions in the ICU. The application of reactive control loop results in a reduction of 8.61% of the average execution time of microbiology results. The combined application of the reactive and deliberative control loop results in an average query time reduction of 10.92% and the combination of reactiv

关键词： execution time Control Loop Resource Description Framework Query execution average execution time

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Functionally-Generalised MOQA Operations

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ELECTRONIC NOTES IN THEORETICAL COMPUTER SCIENCE 2009年第C期225卷 421-439页

作者： Vallee, Thierry Georgia Southern Univ Dept Math Sci Statesboro GA 30460 USA

The programming language MOQA was designed by Michel Schellekens in [5,7] specifically to facilitate the average execution time analysis of its programs. MOQA is based on a special data structure and an associated suite of operations. This special data structure, said here as labeled partial orders (lpo), is a pair (P, l) such that P = (X, E) is a poset andl : X -> L a strictly increasing bijection between X and a totally ordered set L. MOQA basic operations are defined on lpo's under some restrictions. These restrictions guarantee the main property of MOQA's operations, said here as Automatic Random Preservation. Random Preservation (RP) is a fundamental property used to calculate the average time of programs, opening the way to a (semi-)automation of this calculation in MOQA. In the paper, we introduce functionally-generalised versions of some MOQA basic operations. Those versions are defined with more natural restrictions and in a more general way than the correspondent MOQA operations. As a consequence of their greater generality, they are not RP on all their domains in contrast to MOQA operations. Nevertheless, they have two good properties. The first one is that they have the intended semantics. For instance, the generalised split actually splits any lpo into two parts using a label as a pivot. The second one is that the subdomains on which they are RP are strictly bigger than the domains where MOQA basic operations are defined and RP. A tractable characterisation of those RP subdomains is an open problem and its solution would be an important step toward an implementation of the functionally-generalised operations as Automatic RP operations.

关键词： Posets average execution time Program Semantics Data Structure Operations

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On the expected execution time for a class of non uniform recurrence equations mapped onto 1d regular arrays

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Parallel Algorithms and Applications 1993年第4期1卷 303-314页

作者： Aleksandrov, Vassil Fidanova, Stefka Center of Computer Science and Technology Bl. 25A 1113 Sofia Acad G. Bonchev Str. Bulgaria

A special class non uniform recurrent equations produced data dependency graphs which involve nonconstant data dependencies on the input data f(i) is considered. The mathematical expectation of the execution time for the problems described by such recurrences under the assumption that f(i) are uniformly distributed is found. In case when f(i) are exponentially distributed it is proved that the mathematical expectation of time tends to the optimal one. © 1993, Taylor & Francis Group, LLC. All rights reserved.

关键词： average execution time non-uniform recurrence equations Systolic arrays

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A Heuristic Virtual Machine Scheduling Algorithm in Cloud Data Center 3

A Heuristic Virtual Machine Scheduling Algorithm in Cloud Da...

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IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)

作者： Liang, Bin Dong, Xiaoshe Zhang, Xingjun Xi An Jiao Tong Univ Xian 710049 Shaanxi Peoples R China Xian Shiyou Univ Xian 710065 Shaanxi Peoples R China

ISBN: (纸本)9781538662434

As a new model of data storage and computing, cloud data center attracts much attention. Task scheduling is always a hot topic in the research of cloud data center. At the same time, As the cost of cloud data center increases, the cost for users to use cloud data center becomes higher and higher. Less execution time means lower cost. Based on the above reasons, a short average execution time task scheduling algorithm is proposed in this paper, which can reduce the average execution time of the cloud data center according to the difference of the execution time of cloud tasks in the data center, and the effect of this method is more obvious with the increase of cluster size and the increase of the number of tasks. Finally, the algorithm is simulated in cloudsim to verify the effectiveness of the algorithm.

关键词： cloud data center low cost scheduling algorithm average execution time

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Computing with near data

Computing with near data

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Proceedings of the ACM on Measurement and Analysis of Computing Systems

作者： Zhao, Hui Tang, Xulong Jung, Myoungsoo Kandemir, Mahmut Taylan Karakoy, Mustafa Pennsylvania State University State CollegePA16801 United States University of North Texas DentonTX76203 United States Yonsei University Seoul Korea Republic of TOBB University of Economics and Technology Ankara Turkey

One cost that plays a signiicant role in shaping the overall performance of both single-threaded and multi-thread applications in modern computing systems is the cost of moving data between compute elements and storage elements. Traditional approaches to address this cost are code and data layout reorganizations and various hardware enhancements. More recently, an alternative paradigm, called Near Data Computing (NDC) or Near Data Processing (NDP), has been shown to be efective in reducing the data movements costs, by moving computation to data, instead of the traditional approach of moving data to computation. Unfortunately, the existing Near Data Computing proposals require signiicant modiications to hardware and are yet to be widely adopted. In this paper, we present a software-only (compiler-driven) approach to reducing data movement costs in both single-threaded and multi-threaded applications. Our approach, referred to as Computing with Near Data (CND), is built upon a concept called “recomputation, in which a costly data access is replaced by a few less costly data accesses plus some extra computation, if the cumulative cost of the latter is less than that of the costly data access. If implemented carefully, CND can successfully trade of data access with computation, and considering the continuously increasing latency gap between the two, doing so can signiicantly reduce the execution latencies of both sequential and parallel application programs. We i) quantify the intrinsic recomputability of a set of single-threaded and multi-threaded applications, ii) propose a practical, compiler-driven approach that automatically transforms a given application code fragment to a version that employs recomputation, iii) discuss an optimization strategy that increases recomputability; and iv) compare CND, both qualitatively and quantitatively, against NDC. Our experimental analysis of CND reveals that i) the average recomputability across our benchmarks is 51.1%, ii) our

关键词： Data reduction Application programs Cost reduction Diamonds Digital storage Hardware Program compilers average execution time Data movements Experimental analysis Manycore systems Multi threaded applications Parallel application programs Recomputation Traditional approaches

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Implementation of 3D spatial indexing and compression in a large-scale molecular dynamics simulation database for rapid atomic contact detection

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BMC BIOINFORMATICS 2011年第1期12卷 1-11页

作者： Toofanny, Rudesh D. Simms, Andrew M. Beck, David A. C. Daggett, Valerie Univ Washington Dept Bioengn Seattle WA 98195 USA Univ Washington Biomed & Hlth Informat Program Seattle WA 98195 USA Univ Washington eSci Inst Seattle WA 98195 USA Univ Washington Dept Chem Engn Seattle WA 98195 USA

Background: Molecular dynamics (MD) simulations offer the ability to observe the dynamics and interactions of both whole macromolecules and individual atoms as a function of time. Taken in context with experimental data, atomic interactions from simulation provide insight into the mechanics of protein folding, dynamics, and function. The calculation of atomic interactions or contacts from an MD trajectory is computationally demanding and the work required grows exponentially with the size of the simulation system. We describe the implementation of a spatial indexing algorithm in our multi-terabyte MD simulation database that significantly reduces the run-time required for discovery of contacts. The approach is applied to the Dynameomics project data. Spatial indexing, also known as spatial hashing, is a method that divides the simulation space into regular sized bins and attributes an index to each bin. Since, the calculation of contacts is widely employed in the simulation field, we also use this as the basis for testing compression of data tables. We investigate the effects of compression of the trajectory coordinate tables with different options of data and index compression within MS SQL SERVER 2008. Results: Our implementation of spatial indexing speeds up the calculation of contacts over a 1 nanosecond (ns) simulation window by between 14% and 90% (i.e., 1.2 and 10.3 times faster). For a 'full' simulation trajectory (51 ns) spatial indexing reduces the calculation run-time between 31 and 81% (between 1.4 and 5.3 times faster). Compression resulted in reduced table sizes but resulted in no significant difference in the total execution time for neighbour discovery. The greatest compression (similar to 36%) was achieved using page level compression on both the data and indexes. Conclusions: The spatial indexing scheme significantly decreases the time taken to calculate atomic contacts and could be applied to other multidimensional neighbor discovery problems. The

关键词： execution time Molecular Dynamic Simulation Protein Data Bank Total execution time average execution time

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Application and Practice of Intelligent Algorithms in Computer Software Design and Testing

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Procedia Computer Science 2024年 243卷 708-715页

作者： Yun Zhang Wuhan Polytechnic Wuhan Hubei 430074 China

With the rapid development of information technology, software has become a part of social life. However, for complex software systems, traditional design and testing methods face a series of problems such as low testing coverage and low efficiency. The main purpose of this article is to study the optimization of software design and testing processes using intelligent algorithms, in order to improve the quality and efficiency of software. This article established a real-time optimization and feedback adjustment mechanism during the software design and testing phase, enabling intelligent algorithms to respond promptly to the environment and requirements. Monitoring and control strategies were designed to ensure the algorithm runs stably for a long time. Test method 1 (traditional method) had 100 test cases, a total execution time of 1200 seconds, and an average execution time of 12 seconds per test case; test method 2 (Intelligent Algorithm Optimization) had 100 test cases, a total execution time of 800 seconds, and an average execution time of 8 seconds per test case. The research results of this article play a good promoting role in improving software testing coverage and software structure optimization, and also provide new ideas and methods for the promotion of intelligent algorithms in software engineering.

关键词： Computer Software Design Software Testing Intelligent Algorithms average execution time

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CPU-GPU hybrid accelerating the Zuker algorithm for RNA secondary structure prediction applications

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BMC GENOMICS 2012年第Sup1期13卷 S14-S14页

作者： Lei, Guoqing Dou, Yong Wan, Wen Xia, Fei Li, Rongchun Ma, Meng Zou, Dan Natl Univ Def Technol Dept Comp Sci Natl Lab Parallel & Distributed Proc Changsha 410073 Hunan Peoples R China

Background: Prediction of ribonucleic acid (RNA) secondary structure remains one of the most important research areas in bioinformatics. The Zuker algorithm is one of the most popular methods of free energy minimization for RNA secondary structure prediction. Thus far, few studies have been reported on the acceleration of the Zuker algorithm on general-purpose processors or on extra accelerators such as Field Programmable Gate-Array (FPGA) and Graphics Processing Units (GPU). To the best of our knowledge, no implementation combines both CPU and extra accelerators, such as GPUs, to accelerate the Zuker algorithm applications. Results: In this paper, a CPU-GPU hybrid computing system that accelerates Zuker algorithm applications for RNA secondary structure prediction is proposed. The computing tasks are allocated between CPU and GPU for parallel cooperate execution. Performance differences between the CPU and the GPU in the task-allocation scheme are considered to obtain workload balance. To improve the hybrid system performance, the Zuker algorithm is optimally implemented with special methods for CPU and GPU architecture. Conclusions: Speedup of 15.93x over optimized multi-core SIMD CPU implementation and performance advantage of 16% over optimized GPU implementation are shown in the experimental results. More than 14% of the sequences are executed on CPU in the hybrid system. The system combining CPU and GPU to accelerate the Zuker algorithm is proven to be promising and can be applied to other bioinformatics applications.

关键词： Graphic Processing Unit Task Allocation average execution time Graphic Processing Unit Architecture Graphic Processing Unit Device

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