- Jul 27, 2002
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Intel engineers probably should have kept this research in house. Or at least should have consulted a PR team prior to publishing it.
http://www.pcworld.com/article/1997...pu_outperforms_32ghz_core_i7.html?tk=rss_news
NVIDIA responds:
http://blogs.nvidia.com/ntersect/20...-to-14-times-faster-than-cpus-says-intel.html
The paper can be found here:
Debunking the 100X GPU vs. CPU myth: an evaluation of throughput computing on CPU and GPU
http://www.pcworld.com/article/1997...pu_outperforms_32ghz_core_i7.html?tk=rss_news
Intel researchers have published the results of a performance comparison between their latest quad-core Core i7 processor and a two-year-old Nvidia graphics card, and found that the Intel processor can't match the graphics chip's parallel processing performance.
On average, the Nvidia GeForce GTX 280 -- released in June 2008 -- was 2.5 times faster than the Intel 3.2GHz Core i7 960 processor, and more than 14 times faster under certain circumstances, the Intel researchers reported in the paper, called "Debunking the 100x GPU vs. CPU myth: An evaluation of throughput computing on CPU and GPU."
NVIDIA responds:
http://blogs.nvidia.com/ntersect/20...-to-14-times-faster-than-cpus-says-intel.html
Its a rare day in the world of technology when a company you compete with stands up at an important conference and declares that your technology is *only* up to 14 times faster than theirs. In fact in all the 26 years Ive been in this industry, I cant recall another time Ive seen a company promote competitive benchmarks that are an order of magnitude slower.
The paper can be found here:
Debunking the 100X GPU vs. CPU myth: an evaluation of throughput computing on CPU and GPU
Recent advances in computing have led to an explosion in the amount of data being generated. Processing the ever-growing data in a timely manner has made throughput computing an important aspect for emerging applications. Our analysis of a set of important throughput computing kernels shows that there is an ample amount of parallelism in these kernels which makes them suitable for today's multi-core CPUs and GPUs. In the past few years there have been many studies claiming GPUs deliver substantial speedups (between 10X and 1000X) over multi-core CPUs on these kernels. To understand where such large performance difference comes from, we perform a rigorous performance analysis and find that after applying optimizations appropriate for both CPUs and GPUs the performance gap between an Nvidia GTX280 processor and the Intel Core i7-960 processor narrows to only 2.5x on average. In this paper, we discuss optimization techniques for both CPU and GPU, analyze what architecture features contributed to performance differences between the two architectures, and recommend a set of architectural features which provide significant improvement in architectural efficiency for throughput kernels.