Evaluate GPU acceleration for financial models

GPU-enabled pools can accelerate financial models that perform enough parallel numerical work to offset data transfer and initialization costs. A model doesn't benefit from a GPU only because its simulation trials run independently. Profile the calculation before you change the pool.

Identify a suitable calculation

A GPU is more likely to help when the model:

• Applies the same numerical operations across large arrays of scenarios or paths.
• Has enough work per task to keep the GPU busy.
• Transfers a small amount of data compared with the amount of calculation.
• Uses a framework or implementation that already supports the target GPU runtime.

A CPU pool can be more efficient when tasks are short, branch heavily, transfer large datasets, or spend most of their time reading storage.

Compare configurations

Use the same model version, input, partition count, and validation criteria for each benchmark. Compare:

• Pool allocation and node initialization time.
• Model and driver initialization time.
• Task duration and throughput.
• Failure and retry rates.
• Total VM cost per completed and validated run.

Start with a small pool and confirm numerical results against the CPU implementation. Different hardware or numeric libraries can produce small floating-point differences, so define acceptable tolerances before testing.

Prepare a GPU pool

Use a supported GPU-enabled VM size with an image that provides the required NVIDIA drivers. Choose a preconfigured Azure Marketplace image or install the drivers in a custom image. Don't carry forward image names, driver versions, or CUDA versions from older samples without validating current support.

For current pool requirements and examples, see Use GPU-enabled VM sizes in a Batch pool.

GPU VMs can have limited regional capacity and separate quota requirements. Review Batch capacity planning before you schedule a production run.

Next step

Package financial models with containers