In this paper, an in situ power analysis profiling over time for general purpose graphics processing units (GPGPU) is presented. Based on this method the power consumption of different modes of operations like data transfer between GPU and host CPU, basic single precision floating point arithmetic operations (addition, subtraction, multiplication) on the multiprocessor units and instructions for shared and global memory access can be measured. There is a factor of 2 difference in power dissipation between various instructions and mode of operations of the GPGPUs. These measurements provide data for an instruction based power estimation of GPU software. It turns out that the power profile over time also gives a good understanding on which section of the program is executed at a certain point in time. The experimental results have been derived from two GPU architectures, namely the GT200 and the GF100 architecture.
Modern massively parallel graphics cards (GPGPUs) offer a promise of dramatically reducing computation times of numerically-intensive dataparallel algorithms. As cards that are easily integrated into desktop PCs, they can bring computational power previously reserved for computer clusters to the office space. High performance rates make GPGPUs a very attractive target platform for scientific simulations. In this paper we present the lessons learned during the parallelization of a finite-difference time-domain method, an inherently data-parallel algorithm frequently used for numerical computations, on the state of the art graphics hardware.
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