Benchmark Ascend hardware runtime performance across host and device timings.
npx skills add https://github.com/hw-native-sys/simpler --skill benchmark-hw-native-sys
Please help me install this Agent Skill. Skill: benchmark Source: https://github.com/hw-native-sys/simpler/tree/main/.claude/skills/benchmark Command: npx skills add https://github.com/hw-native-sys/simpler --skill benchmark-hw-native-sys
Benchmarking runtime performance on Ascend hardware to quantify platform efficiency across workloads and runtimes.
Run the vector performance suite on your Ascend hardware to begin benchmarking.
High-intent search queries and answers about installing and using this skill.
To benchmark runtime performance on Ascend hardware, run the vector performance suite to quantify dispatch and execution efficiency across graph-based runtimes and workloads.
Ascend runtime benchmarking produces Host, Device, Total, Sched, and Orch timing metrics, providing an end-to-end measurement of host and device execution efficiency.
You can compare host vs device timings for tensormap_and_ringbuffer workloads by running the benchmarking framework to capture detailed breakdowns and compare against baseline points.
Yes, you need access to the Ascend toolkit and the benchmarking framework described in the docs to measure graph-based runtime performance and produce detailed timing metrics.
Use runtime benchmarking when validating performance changes across workloads, quantifying platform efficiency, or comparing baseline timings for graph-based runtimes on Ascend hardware.
Yes, hardware teams validate dispatch and execution efficiency changes by measuring end-to-end host and device timings across tensormap_and_ringbuffer workloads using the benchmarking framework.
AscendInstall and configure the CANN toolkit on Ascend NPU hardware.
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MsabihProfile ExecuTorch model execution with per-operator timings and ETDump export.
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