High-Performance Compute Optimization for Simulation Workloads
Project CoreAccelerateX
Client:
NDA-protected enterprise
Duration:
9 months
Team:
2 HPC engineers, 1 GPU specialist, 1 performance engineer
The Problem
Simulation workloads required hours or days to complete, slowing down research velocity and operational throughput.
What We Did
Identified bottlenecks, rewrote compute kernels in CUDA/C++, optimized numerical routines, and introduced distributed parallel execution.
Outcome
A 4× performance boost on core workloads, drastically faster iteration cycles, and more efficient use of compute resources.
Operational Impact
Faster simulation cycles. Improved solver accuracy. Reduced compute overhead.
Key Challenges
1
Numerical Stability
Ensuring optimized solvers remained accurate during extreme parameter ranges.
2
GPU Saturation
Maximizing GPU utilization without memory fragmentation.
What Made This Work
HPC-First Engineering
Compute kernel optimization at the lowest levels for maximum performance.
Kernel-Level Optimizations
Custom CUDA kernels achieving 4× speedup on critical workloads.
Distributed Parallel Execution
Workload distribution across HPC nodes for scalable compute.