3 ms·

There are a lot of excited posts here about this announcement! For someone that doesn't use GPU's in everyday life, can someone explain why this is great and m

by dylanz 10y ago

There are a lot of excited posts here about this announcement! For someone that doesn't use GPU's in everyday life, can someone explain why this is great and maybe touch on the current landscape around GPU usage and the cost landscape?

mattlondon 10y ago
GPUs for certain workloads are much more powerful than CPUs, meaning you can get stuff done much quicker. Machine Learning is one such field, batch-rendering for CG studios etc is another. You could throw 100 CPUs at something that perhaps 1 GPU might do in the same time (made up numbers but it gives you an idea of why people want it)
slizard 10y ago
The excitement comes from the fact that some workloads benefit greatly from GPUs -- mostly those where the GPU arch offers some niche-advantage that can give GPUs >=10x perf[/W]. A good example is deep learning where Intel is struggling to compete [1], and even in linear algebra there is a 4-5x power efficiency difference [2, slide 6]. At the same time, in many other complex applications the performance/W/$ advantage has not been all that clear, especially since GPUs have been quite behind in process technology. A good example is a simulation code I work on where a 2-4x speedup from GPUs, which (due to inherent overhead of using accelerators) gradually vanishes in strong scaling, means that when price is considered, only cheap consumer cards can imrpove the performance/buck metric significantly (that's time-to-solution/buck in our case), professional cards don't offer significant significant improvements other than performance density [3, figures 6,7]. There are plenty more examples that NVIDIA collects [4], but always take the claims with a grain of salt, especially the ones with "incredible" >10x speedup claims :) Last, I'd note that with the recent 14-16nm jump, the gap between the traditional CPUs and the simpler accelerator architectures has increased (I've just seen MAGMA BLAS on Tesla P100 results which show >10x GFlops/W, more than double that of the previous arch) and I expect it to keep increasing partly due to the manufacturing/process technology gap shrinking between Intel and the rest and partly due to architecture and programmability improvements of accelerators. [1] https://www.nvidia.com/object/gpu-accelerated-applications-tensorflow-benchmarks.html https://www.nvidia.com/object/gpu-accelerated-applications-t... [2] http://on-demand.gputechconf.com/gtc/2015/presentation/S5476-Stanimire-Tomov.pdf http://on-demand.gputechconf.com/gtc/2015/presentation/S5476... [3] https://www.academia.edu/13753737/Best_bang_for_your_buck_GPU_nodes_for_GROMACS_biomolecular_simulations https://www.academia.edu/13753737/Best_bang_for_your_buck_GP... [4] https://www.nvidia.com/object/gpu-applications.html https://www.nvidia.com/object/gpu-applications.html

by dylanz 10y ago

mattlondon 10y ago

GPUs for certain workloads are much more powerful than CPUs, meaning you can get stuff done much quicker. Machine Learning is one such field, batch-rendering for CG studios etc is another. You could throw 100 CPUs at something that perhaps 1 GPU might do in the same time (made up numbers but it gives you an idea of why people want it)

slizard 10y ago

The excitement comes from the fact that some workloads benefit greatly from GPUs -- mostly those where the GPU arch offers some niche-advantage that can give GPUs >=10x perf[/W]. A good example is deep learning where Intel is struggling to compete [1], and even in linear algebra there is a 4-5x power efficiency difference [2, slide 6]. At the same time, in many other complex applications the performance/W/$ advantage has not been all that clear, especially since GPUs have been quite behind in process technology. A good example is a simulation code I work on where a 2-4x speedup from GPUs, which (due to inherent overhead of using accelerators) gradually vanishes in strong scaling, means that when price is considered, only cheap consumer cards can imrpove the performance/buck metric significantly (that's time-to-solution/buck in our case), professional cards don't offer significant significant improvements other than performance density [3, figures 6,7]. There are plenty more examples that NVIDIA collects [4], but always take the claims with a grain of salt, especially the ones with "incredible" >10x speedup claims :) Last, I'd note that with the recent 14-16nm jump, the gap between the traditional CPUs and the simpler accelerator architectures has increased (I've just seen MAGMA BLAS on Tesla P100 results which show >10x GFlops/W, more than double that of the previous arch) and I expect it to keep increasing partly due to the manufacturing/process technology gap shrinking between Intel and the rest and partly due to architecture and programmability improvements of accelerators. [1] https://www.nvidia.com/object/gpu-accelerated-applications-tensorflow-benchmarks.html https://www.nvidia.com/object/gpu-accelerated-applications-t... [2] http://on-demand.gputechconf.com/gtc/2015/presentation/S5476-Stanimire-Tomov.pdf http://on-demand.gputechconf.com/gtc/2015/presentation/S5476... [3] https://www.academia.edu/13753737/Best_bang_for_your_buck_GPU_nodes_for_GROMACS_biomolecular_simulations https://www.academia.edu/13753737/Best_bang_for_your_buck_GP... [4] https://www.nvidia.com/object/gpu-applications.html https://www.nvidia.com/object/gpu-applications.html