3.0.24989.8 MB
Apache-2.0
classic
core20
Peak compute, memory bandwidth and latency of CPUs, GPUs and NPUs
clpeak (compute latency peak) measures the peak compute throughput, memory
bandwidth and latency that a machine's CPUs, GPUs and NPUs actually reach.
It uses small, tight kernels alongside each vendor's own tuned libraries,
and runs the same tests through every API that exposes a device, so the
results line up side by side.
What it measures:
- Compute: fp32, fp16, fp64, mixed-precision, bf16, integer and
dot-product rates
- Matrix engines: cooperative matrices, tensor cores and CPU matrix units
- Bandwidth: device, local and image memory, host transfers, and CPU cache
and DRAM bandwidth per level
- Latency: kernel launch, memory latency per cache level, atomics and
branch mispredicts
- AI workloads: matmul, convolution and a transformer block per data
type, each with its numeric error against fp32
This snap has the Vulkan, OpenCL and native CPU backends, and the ONNX
Runtime and LiteRT backends, which use a runtime installed on the host
(--onnx-lib, --litert-lib). Builds with the CUDA, ROCm and oneAPI backends
are on the GitHub releases page.
It has two front-ends over the same engine: the clpeak command (clpeak
--help) and the desktop app, clpeak.clpeak-gui, which is also in the app
launcher on x86-64. Runs can be saved as JSON (-o run.clpeak.json) and
compared with a later run (--compare).
Classic confinement lets clpeak reach the GPU drivers and device nodes it
benchmarks.
Source, issues and reference results: https://github.com/krrishnarraj/clpeak
bandwidth and latency that a machine's CPUs, GPUs and NPUs actually reach.
It uses small, tight kernels alongside each vendor's own tuned libraries,
and runs the same tests through every API that exposes a device, so the
results line up side by side.
What it measures:
- Compute: fp32, fp16, fp64, mixed-precision, bf16, integer and
dot-product rates
- Matrix engines: cooperative matrices, tensor cores and CPU matrix units
- Bandwidth: device, local and image memory, host transfers, and CPU cache
and DRAM bandwidth per level
- Latency: kernel launch, memory latency per cache level, atomics and
branch mispredicts
- AI workloads: matmul, convolution and a transformer block per data
type, each with its numeric error against fp32
This snap has the Vulkan, OpenCL and native CPU backends, and the ONNX
Runtime and LiteRT backends, which use a runtime installed on the host
(--onnx-lib, --litert-lib). Builds with the CUDA, ROCm and oneAPI backends
are on the GitHub releases page.
It has two front-ends over the same engine: the clpeak command (clpeak
--help) and the desktop app, clpeak.clpeak-gui, which is also in the app
launcher on x86-64. Runs can be saved as JSON (-o run.clpeak.json) and
compared with a later run (--compare).
Classic confinement lets clpeak reach the GPU drivers and device nodes it
benchmarks.
Source, issues and reference results: https://github.com/krrishnarraj/clpeak
Update History
3.0.1 (491) → 3.0.2 (498)21 Oct 2026, 11:00 UTC
3.0.0 (484) → 3.0.1 (491)4 Oct 2026, 15:30 UTC
2.1.4 (455) → 3.0.0 (484)26 Sept 2026, 10:45 UTC
2.1.3 (445) → 2.1.4 (455)28 Aug 2026, 01:30 UTC
2.1.0 (428) → 2.1.3 (445)24 Aug 2026, 10:15 UTC
2.0.15 (396) → 2.1.0 (428)18 Aug 2026, 11:00 UTC
2.0.14 (389) → 2.0.15 (396)25 Jun 2026, 08:15 UTC
2.0.12 (379) → 2.0.14 (389)23 Jun 2026, 14:15 UTC
1.1.2 (256) → 2.0.12 (379)14 Jun 2026, 08:15 UTC
1.1.2 (256)1 Apr 2026, 21:28 UTC
10 Jul 2019, 13:56 UTC
21 Oct 2026, 10:44 UTC
1 Apr 2026, 21:28 UTC