Optimizing Financial High-Performance Computing with GPUs on AWS: Case Study - Sandeep Kanao
Abstract:
The financial sector, especially in the context of market risk assessment, heavily relies on computational applications. However, many of these applications are traditionally built on outdated CPU technology, leading to performance limitations. To address these limitations, financial institutions often resort to purchasing licenses for third-party grid applications like SGE (Sun Grid Engine) and Data Synapse. Additionally, high-performance computing (HPC) applications in this domain are typically developed using tools like the GCC (GNU Compiler Collection) or Windows C#, which lack support for advanced hardware technologies such as GPUs (Graphics Processing Units) or TPUs (Tensor Processing Units).
This study explores the challenges and opportunities in optimizing high-compute applications in financial computing. It investigates the utilization of NVIDIA CUDA on AWS GPU instances to overcome these challenges and improve performance significantly. The study focuses on performance benchmark comparisons between native CPU compilation and the substantial performance gains achieved through the utilization of Amazon Cloud (NVIDIA GPU) infrastructure.
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