Not really fair, as I should have really used SIMD on the CPU, or somesuch, to use the CPU’s (limited) parallel processing functionality.
Let’s be benevolent and allow the CPU to use all it’s 8 threads in it’s imaginary parallel processing (at about 4x the clock speed of the GPU… still gives a 2.5 times throughput speed factor to the GPU (but would be nowhere near that low in reality).
Price:
CUDA for Engineers: An Introduction to High-Performance Parallel Computing
Pearson Addison Wesley Prof (as of April 9, 2020 - More infoProduct prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on [relevant Amazon Site(s), as applicable] at the time of purchase will apply to the purchase of this product.)
Price: $44.99
Hands-On GPU Programming with Python and CUDA: Explore high-performance parallel computing with CUDA
(as of April 9, 2020 - More infoProduct prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on [relevant Amazon Site(s), as applicable] at the time of purchase will apply to the purchase of this product.)
Price: $26.99
CUDA by Example: An Introduction to General-Purpose GPU Programming
(as of April 9, 2020 - More infoProduct prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on [relevant Amazon Site(s), as applicable] at the time of purchase will apply to the purchase of this product.)
Price: $30.09
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so interesting, thanks a lot
Gtx 780 and 23 gflops? Did you compile for debug or release?
I get 202gflops from k420 which has just 1 smx or 192 cuda cores.
Compiling for release would also make cpu use simd on applicable parts of codes.