TECHNOLOGY PROFILE · TECH-0045
CUTLASS
CUDA templates and abstractions for high-performance linear algebra.
Reference link only — not reviewed this edition. This profile preserves the prepared catalog’s original descriptions. Advantages depend on the workload; comparisons are editorial, not benchmark results.
Where it has an edge
Exposes GPU matrix-kernel building blocks beyond generic library calls without starting entirely from scratch.
Best-fit problems
Custom GEMM and mixed-precision GPU kernel engineering.
Tradeoffs & cautions
Requires significant GPU expertise and ties work to supported NVIDIA architectures.
What to test before choosing
Benchmark exact shapes, dtypes and devices after warm-up; verify numerical tolerances and end-to-end speed.
Runtime & hardware
Hardware-specific; see profile
Access & licensing
Mixed open/proprietary components; verify terms
Execution
Local / self-hosted
Lifecycle
Not independently audited
Comparison basis
Editorial problem-fit judgement; no head-to-head benchmark performed
Connected concepts
Alternatives to investigate
Triton GPU language · cuDNN