An Experimental Analysis of General Purpose Computing with Commodity Data-Parallel Multicore Processors

Multicore processors have recently emerged as a better solution to leverage the benefits of semiconductor technology scaling than traditional architectures based on a single CPU. Meanwhile, graphics processing units (GPUs) are increasingly used not only for graphics rendering but also for general purpose computing. High-end GPUs also follow the trend of integrating multiple processing units on a single chip while adding high memory-access bandwidth. Exploiting the computational potential of multicore architectures requires new programming paradigms and tools that are optimized for parallel computing rather than conventional (single-processor) sequential programming. We select three non-graphics benchmark applications to conduct head-to-head performance comparisons between two data-parallel multicore processors: a state-of-the-art GPU, the NVIDIA GeForce 8800 GTX, and a leading heterogeneous multicore processor, the IBM Cell Broadband Engine. We also present a preliminary analysis of the trade-offs between programmability and code efficiency for these benchmarks by comparing implementations that are based on processor-specific software development environments with those obtained with a portable multi-core development platform.

By: Rebecca Collins; Cheng-Hong Li; Luca P. Carloni; Kevin J. Nowka; Eugen Schenfeld

Published in: RC25070 in 2010


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