Examples from our work
Performance problems exist at every scale.
Sometimes the right intervention changes a few lines of code.
Sometimes it changes how the software uses hardware.
Sometimes it changes the computational method itself.
Kernel level
Removing a numerical kernel bottleneck
A scientific workflow was spending most of its runtime inside nested
loops over large multidimensional arrays.
Instead of adding compute, we examined how the workload accessed
memory and how operations were organized across its feature,
spatial and temporal dimensions.
The analysis showed that much of the computation could be expressed
as vectorized array operations. The kernel was restructured around
contiguous operations while retaining only the necessary outer loop.
- Bottleneck: loop and memory-access overhead
- Intervention: vectorization and restructuring
- Scope: individual computational kernel
System level
Scaling a computational research pipeline
A computational modeling pipeline had become expensive enough that
runtime was limiting the speed of research and iteration.
We analyzed how computation moved through the full workflow rather
than optimizing individual functions in isolation. Profiling exposed
opportunities for improved parallel execution and hardware acceleration.
The resulting acceleration work combined software restructuring
with parallel and GPU execution, targeted to the expensive operations
that actually benefit from modern hardware.
- Bottleneck: inefficient execution of compute-intensive workloads
- Intervention: vectorization, parallelization and GPU acceleration
- Scope: full computational workflow
Algorithm level
Replacing repeated numerical work with learned surrogates
Sometimes optimizing the implementation is not enough. The expensive
operation itself has to change.
In as scientific machine learning setting, we worked on computational
workflows where expensive numerical operations were repeatedly
evaluated inside a larger simulation or solution process.
Rather than only making those operations execute faster, we
investigated learned surrogate models that approximate the expensive
computation while preserving the numerical behavior required by the
surrounding workflow.
- Bottleneck: fundamentally expensive repeated computation
- Intervention: scientific ML and surrogate modeling
- Scope: computational method itself
