Abaqus Uses Computing Power of GPUs for Faster Simulation

Today’s business requires us to use our time down to the second effectively, so why waste hours? GPUs are developed to increase the performance of applications like Abaqus. Once plugged into clusters or workstations, their parallel computing capabilities enable the program to run nearly twice as fast; more in certain cases. This page gives you all the information you need to learn about this program, to check its ROI for you, and to sign up.

Abaqus/Standard GPU Computing

• SIMULIA announced support for CUDA and GPUs in Abaqus 6.11
  • NVIDIA GPUs include Tesla 20-series and Quadro 6000
• The direct equation solver is accelerated on the GPU
  • More of Abaqus will be moved to GPUs in progressive stages
• Abaqus 6.12 supports multi-GPU, multi-node DMP clusters
  • Linux and Windows OS
  • Flexibility to run jobs on specific GPUs
• Basic system recommendations
  • Large system memory (48GB) to avoid scratch I/O of system matrix
  • Ratio of 1 GPU attached to 1 CPU socket (4-8 cores)

Recommendations for GPU acceleration

• Key factors for model selection for GPU acceleration:
  • Enough work
    • FLOPs, Solid models
  • In-core solution
    • Sufficient system (host) memory
  • Large fronts fit in the GPU memory
    • Super-nodes fit in device memory (6 GB)
    • Size limits will be largely eliminated in Abaqus 6.13
  • Uses direct sparse solver
    • Unsymmetric solve in-development for Abaqus 6.13

Innovate Faster with GPU-Accelerated Abaqus FEA