Storage consists of a 2 TB SSD and an 8 TB HDD. GPU memory is a combined 96 GB and it includes 196 GB of system memory. It is a dual Quadro RTX 8000 workstation with two Intel Xeon Silver 4216 CPUs. The workstation comes from Scan Computers in the UK. We also demonstrate multi-GPU performance in simulations of the Min protein system in E. The bottom line: the workstation is fast and Resolve 16 leverages the dual GPU configuration well. We present parallel efficiency and performance results for simulations using multiple GPUs as system size, particle counts, and number of reactions grow. The AIME A4000 is an enterprise Deep Learning server based on the ASUS ESC4000A-E10, configurable with up to 4 of the most advanced deep learning accelerators and GPUs to enter the Peta FLOPS HPC computing area with more then 4 Peta TensorOps Deep Learning performance.Packed into a dense form factor of 2 height units, EPYC CPU performance, the fastest. We take advantage of high-performance features of CUDA for peer-to-peer GPU memory transfers and evaluate the performance of our algorithms on state-of-the-art GPU devices. We present a new multi-GPU parallel implementation of the MPD-RDME method based on a spatial decomposition approach that supports dynamic load balancing for workstations containing GPUs of varying performance and memory capacity. Multiple GPUs are still used on supercomputers (like in Summit), on workstations to accelerate video (processing multiple videos at once) and 3D rendering, for. Simulations of larger eukaryotic systems exceed the on-board memory capacity of individual GPUs, and long time simulations of modest-sized cells such as yeast are impractical on a single GPU.
#Multi gpu workstation software#
Our previous software enabled simulation of inhomogeneous biochemical systems for small bacteria over long time scales using the MPD-RDME method on a single GPU. Seamlessly scale from GPU workstations to multi-GPU servers and multi-node clusters with Dask.
Simulation of in vivo cellular processes with the reaction–diffusion master equation (RDME) is a computationally expensive task.