Cognizant explains how it uses cloud high-performance computing to bring more compute into capital-markets research, where teams chase higher risk-adjusted return while controlling drawdown and variance. The focus is on turning “too big for one machine” workloads into parallel jobs with tight coupling between compute, storage, and scheduling. https://www.cognizant.com/
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The framing is that many quant and risk workloads behave like NP-hard or stochastic search spaces: portfolio construction, scenario generation, stress testing, and Monte Carlo pipelines quickly explode in state count and data volume. HPC acts as the problem-solver layer that makes iterative model calibration, parameter sweeps, and pricing runs practical at the pace markets demand, without waiting on a single workstation for pricing.
On the architecture side they point to AWS ParallelCluster with the Slurm scheduler to provision and queue workloads, then emphasize an elastic, dynamic cluster model rather than a fixed always-on grid. Each researcher or team can spin up a right-sized cluster with guardrails, and when runs complete the infrastructure is shut down to avoid idle burn; the interview was recorded at Supercomputing SC25 in St Louis, where these cloud-HPC patterns show up across many domains of compute.
They also clarify what AWS “HPC certification” signals in practice: proven competence designing and delivering HPC architectures on AWS, backed by successful real engagements. Cognizant positions this as a repeatable cloud-HPC playbook that extends beyond capital markets into regulated sectors like health sciences, banking, insurance, energy, and aerospace, where governance, security controls, and performance engineering must scale.
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