Add GPUs Accelerate Science Applications NVIDIA 2013 Small Changes Big Speedup Application Code GPU C PU Use GPU to Parallelize ComputeIntensive Functions Rest of Sequential CPU Code ID: 649401
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Slide1
Why GPU ComputingSlide2
GPU
CPU
Add GPUs: Accelerate Science Applications
© NVIDIA 2013Slide3
Small Changes, Big Speed-up
Application Code
+
GPU
C
PU
Use GPU to Parallelize
Compute-Intensive Functions
Rest of SequentialCPU Code
© NVIDIA 2013Slide4
Fastest Performance on Scientific Applications
Tesla K20X Speed-Up over Sandy Bridge CPUs
CPU
results: Dual socket E5-2687w, 3.10
GHz, GPU
results: Dual socket E5-2687w + 2 Tesla K20X GPUs*MATLAB results comparing one i7-2600K CPU vs with Tesla K20 GPUDisclaimer: Non-NVIDIA implementations may not have been fully optimized
Engineering
Earth
Science
Physics
Molecular
Dynamics© NVIDIA 2013Slide5
Why Computing
P
erf/Watt
Matters?
Traditional CPUs are
not economically feasible
2.3
PFlops
7000 homes
7.0
Megawatts
7.0
Megawatts
CPU
Optimized for
Serial Tasks
GPU Accelerator
Optimized
for Many
Parallel Tasks
10x performance/socket
> 5x
energy
efficiency
Era of GPU-accelerated computing
is
here
© NVIDIA 2013Slide6
World’s Fastest, Most Energy Efficient Accelerator
Tesla K20X
vs
Xeon CPU
8x Faster SGEMM
6x Faster DGEMM
Tesla K20X
vs
Xeon Phi90% Faster SGEMM60% Faster DGEMM
© NVIDIA 2013