A survey on resource allocation in high performance distributed computing systems

Abstract

a b s t r a c tAn efficient resource allocation is a fundamental requirement in high performance computing (HPC) systems. Many projects are dedicated to large-scale distributed computing systems that have designed and developed resource allocation mechanisms with a variety of architectures and services. In our study, through analysis, a comprehensive survey for describing resource allocation in various HPCs is reported. The aim of the work is to aggregate under a joint framework, the existing solutions for HPC to provide a thorough analysis and characteristics of the resource management and allocation strategies. Resource allocation mechanisms and strategies play a vital role towards the performance improvement of all the HPCs classifications. Therefore, a comprehensive discussion of widely used resource allocation strategies deployed in HPC environment is required, which is one of the motivations of this survey. Moreover, we have classified the HPC systems into three broad categories, namely: (a) cluster, (b) grid, and (c) aforementioned systems are cataloged into pure software and hybrid/hardware solutions. The system classification is used to identify approaches followed by the implementation of existing resource allocation strategies that are widely presented in the literature.

GPU based parallel FDEM for analysis of cable structures

“…Different types of parallelisation solutions have therefore been explored, including the shared memory parallel computers [26,27], distributed memory parallel computers [28], hardware independent virtual parallel machine framework for FDEM [29], and the MPI static [30] and dynamic space decomposition [31]. Parallelization procedures utilize hardware in similar fashion: concurrent job execution on many processor cores working on a specific part of the domain with communication in-between.…”

Performance Evaluation in a Cloud with the Provisioning of Different Resources Configurations

“…In [5], for instance, the authors present a study about resource allocation among multiple HPC (High Performance Computing) systems like cluster, grid and cloud. Sharkh et al [14] discuss various internal and external factors that should be considered in the resource allocation process.…”

Planning And Allocation of Tasks in a Multiprocessor System as a Multi-Objective Problem and its Resolution Using Evolutionary Programming

“…Examples of such systems are the IBM BlueGene / L [4] and the Intel Paragon [5]. Some of the commercial Multi Computer systems are Multiple Instruction Multiple Data (MIMD) systems with architectures that enable partitions of processor submeshes, and have the advantage of supporting multiple parallel (multi-tasks ) jobs [1,2,3,6]. Parallel jobs are usually represented by a Directed Acyclic Graph (DAG), the nodes express the particular tasks partitioned from an application and the edges represent the inter-task communication [7].…”

“…Parallel jobs are usually represented by a Directed Acyclic Graph (DAG), the nodes express the particular tasks partitioned from an application and the edges represent the inter-task communication [7]. The tasks can be dependent or independent; independent tasks, can be executed simultaneously to minimize processing time, and dependent tasks are cumbersome and must be processed in a pre-defined manner, to ensure that all dependencies are satisfied [6]. In an SIMD mesh, that processes parallel jobs, tasks are planned in the queue by a planning policy (usually being First Come First Serve (FCFS)) [2,3,8,9], they are then assigned to the mesh processor, where they remain until they finish their implementation [7].…”