A GPU-Based Enhanced Genetic Algorithm for Power-Aware Task Scheduling Problem in HPC Cloud

Abstract

Abstract. In this paper, we consider power-aware task scheduling (PATS) in HPC clouds. Users request virtual machines (VMs) to execute their tasks. Each task is executed on one single VM, and requires a fixed number of cores (i.e., processors), computing power (million instructions per second -MIPS) of each core, a fixed start time and non-preemption in a duration. Each physical machine has maximum capacity resources on processors (cores); each core has limited computing power. The energy consumption of each placement is measured for cost calculating purposes. The power consumption of a physical machine is in a linear relationship with its CPU utilization. We want to minimize the total energy consumption of the placements of tasks. We propose here a genetic algorithm (GA) to solve the PATS problem. The GA is developed with two versions: (1) BKGPUGA, which is an adaptively implemented using NVIDIA's Compute Unified Device Architecture (CUDA) framework; and (2) SGA, which is a serial GA version on CPU. The experimental results show the BKGPUGA program that executed on a single NVIDIA® TESLA™ M2090 GPU (512 cores) card obtains significant speedups in comparing to the SGA program executing on Intel® Xeon TM E5-2630 (2.3 GHz) on same input problem size. Both versions share the same GA's parameters (e.g. number of generations, crossover and mutation probability, etc.) and a relative small (10 -11 ) on difference of two finesses between BKGPUGA and SGA. Moreover, the proposed BKGPUGA program can handle large-scale task scheduling problems with scalable speedup under limitations of GPU device (e.g. GPU's device memory, number of GPU cores, etc.).

Parallel approaches of genetic algorithm in the MIC architecture of the Intel Xeon Phi

“…Trên KNC, song song về dữ liệu (data parallelism) sẽ được thực hiện trên tất cả các tác vụ đánh giá (evaluation), lai chéo (crossover), đột biến (mutation), lựa chọn (selection) bằng cách sử dụng các chỉ thị của OpenMP. Tạo ra các số ngẫu nhiên là một chức năng chính trong GA, nhưng hàm rand() không thể thực thi song song, điều này khiến chương trình cực kỳ chậm, thậm chí chậm hơn giải thuật di truyền tuần tự (SGA) 3 . Giải thuật di truyền tuần tự (SGA) là giải thuật di truyền tương tự GAMIC (Offload) chỉ được thực thi trên 1 lõi (1 CPU core hay 1 MIC core).…”