AMD FirePro™ S9300 x2 Accelerator The World’s First GPU Accelerator with 1TB/s Memory Bandwidth Accelerate your most complex HPC workloads in data analytics or seismic processing on the world’s fastest single-precision compute GPU accelerator, the AMD FirePro™ S9300 x2 Server GPU.2,3 Take advantage of the numerous tools and libraries available at your disposal, including ROCm tools, from our developer page at
A recent test was undertaken by one of our customers, CGG. CGG is a leader in cutting-edge geoscience and recently conducted proprietary wave equation modelling benchmarking on several different GPU accelerators, including the new AMD FirePro™ S9300 x2 GPU. As the complexity of the wave equation increased, the performance advantage also grew in favor of the AMD FirePro™ S9300 x2 GPU, to a point where it was 2x faster than any other card tested.4
Chart Provided by CGG
AMD FirePro™ S9100, S9150 and S9170 Accelerators
Those who are looking for great double precision performance can turn to the AMD FirePro™ S9100 series of accelerators. The AMD FirePro™ S9150, powering the #1 ranked supercomputer on the 2014 Green500 list, easily surpasses the competition by offering over 50% more double precision performance than the comparable Tesla K40.5
Watch the video interview of Dr. David Rohr and Professor Lindenstruth talking about the L-CSC cluster, #1 ranked supercomputer on the 2014 Green500.
DGEMM, or Double-precision General Matrix-Matrix multiplication, measures floating point execution rate for double precision, real matrix-matrix multiplication. There are many real-world applications that take advantage of double-precision matrix operations. These include computational fluid dynamics, finite element analysis and structural modelling, and molecular dynamics.
With our AMD OpenCL BLAS implementation, we are able to achieve 2 TFLOPS of sustained DGEMM performance with the AMD FirePro™ S9150 GPU, while the Tesla K40 achieves 1.3 TFLOPS DGEMM.
The AMD FirePro™ S9170 GPU is great for those who need large matrix-matrix multiplication capabilities, where one can take advantage of the large 32GB GDDR5 memory that this card possesses. The Nvidia K80 and K40, with 24GB and 12GB memory, respectively, cannot compute matrices that are larger than what their smaller onboard memory can handle.