A Comparative Study on ASIC, FPGAs, GPUs and General Purpose Processors in the O(N^2) Gravitational N-body Simulation

In this paper, we describe the implementation of gravitational force calculation for N-body simulations in the context of Astrophysics. It will describe high performance implementations on general purpose processors, GPUs, and FPGAs, and compare them using a number of criteria including speed performance, power efficiency and cost of development. These results show that, for gravitational force calculation and many-body simulations in general, GPUs are very competitive in terms of performance and performance per dollar figures, whereas FPGAs are competitive in terms of performance per Watt figures.2009 NASA/ESA Conference on Adaptive Hardware and Systems (AHS) : San Francisco, CA, USA, 2009.07.29-2009.08.

FPGA Based Acceleration of Matrix Decomposition and Clustering Algorithm Using High Level Synthesis

FPGAs have shown great promise for accelerating computationally intensive algorithms. However, FPGA-based accelerator design is tedious and time consuming if we rely on traditional HDL based design method. Recent introduction of Altera SDK for OpenCL (AOCL) high level synthesis tool enables developers to utilize FPGA’s potential without long development time and extensive hardware knowledge. AOCL is used in this thesis to accelerate computationally intensive algorithms in the field of machine learning and scientific computing. The algorithms studied are k-means clustering, k-nearest neighbour search, N-body simulation and LU decomposition. The performance and power consumption of the algorithms synthesized using AOCL for FPGA are evaluated against state of the art CPU and GPU implementations. The k-means clustering and k-nearest neighbor kernels designed for FPGA significantly out-performed optimized CPU implementations while achieving similar or better power efficiency than that of GPU