Case study
HPC/Infra
CUDA stencil benchmark & GPU kernel optimization
Delivered a CUDA stencil benchmark suite and kernel optimizations demonstrating measurable GPU performance gains on representative HPC patterns.
HPC engineering engagement · ~5 min read
At a glance
Speedup vs baseline
[TBD: x]
Optimized stencil kernels vs naive CUDA port
Roofline attainment
[TBD: %]
Achieved fraction of memory/compute roofline bound
Problem sizes covered
[TBD: N configs]
Grid dimensions and halo widths in benchmark matrix
Multi-GPU scaling
[TBD: efficiency %]
Strong scaling efficiency where decomposition applied
Problem
A research-oriented engagement needed reproducible GPU performance baselines for stencil-heavy scientific codes before committing to larger porting or cluster procurement decisions. Ad hoc kernel tweaks produced inconsistent numbers that were hard to compare across problem sizes or hardware generations.
Naive CUDA ports of legacy CPU stencil loops left significant bandwidth and occupancy on the table. Downstream science and engineering teams needed a documented methodology, not one-off optimizations that could not be reproduced or extended.
Approach
- A parameterized benchmark harness built stencil drivers with configurable dimensionality, halo width, and timing hooks suitable for roofline-style analysis.
- Memory and occupancy tuning reworked access patterns, block sizing, and shared-memory staging to improve bandwidth utilization and SM occupancy on representative kernels.
- Multi-GPU decomposition partitioned domains where applicable and measured scaling behavior with explicit communication cost accounting.
- Adoption documentation packaged methodology, build instructions, and interpretation guides so client teams could rerun benchmarks on new GPUs without re-engaging for every hardware refresh.
Results
[TBD: Insert validated metrics before publish.]
- Benchmark harness produced comparable timings across problem sizes for planning and regression after code changes.
- Optimized kernels demonstrated measurable speedups over naive CUDA baselines on target GPU classes.
- Roofline-oriented reporting clarified whether remaining headroom was memory- or compute-bound for each stencil variant.