An Analysis of GPU Parallel Computing

Parallel systems are becoming ubiquitous in the world of computing as evidenced by multi-core processors, heterogeneous Cell broadband engine, and highly parallel graphics processing units (GPUs). All parallel systems share a requirement that parallel programming is necessary to leverage multiple cores. As a result of this trend, multi-core CPUs are no longer a clear winner due to its peaked clock frequency and programming effort involved in parallelizing code for multi-core architecture. Given such drawbacks, dataparallel applications might benefit from GPU assisted computing. GPUs are the most popular and inexpensive accelerators. To evaluate GPU-based computing, a floating-point intensive algorithm for a radar imaging application is chosen for analysis. The paper attempts to present a fair performance comparison of CPU and GPU implementations.