Menü
SkillsMP hat 8 Skills aus teddyjfpender/stwo-zig gesammelt. Öffne einen Skill, um Quelle und Details zu prüfen.
Es werden 8 von 8 gesammelten Skills angezeigt.
Isolate a Metal kernel and measure real GPU time, pipeline reflection (occupancy limits, execution width, threadgroup memory), and approximate achieved bandwidth via the generic runner; wrap whole commands in Metal System Trace. Use for GPU performance…
Quellsprache: Englisch
Implement and review CUDA prover kernels under exact field, transcript, AOT, ABI, memory-traffic, and occupancy contracts. Use after profiling identifies a dominant CUDA kernel or pass.
Quellsprache: Englisch
Design generic, process-resident CUDA proving architectures from ProofProgram and CudaPlan evidence. Use for runtime ownership, arenas, caching, streams, graphs, AOT modules, pass reduction, proof concurrency, and frontend/backend boundaries.
Quellsprache: Englisch
Attribute CUDA prover latency with product stage events, NVTX, Nsight Systems, and Nsight Compute while preserving an uninstrumented verified-request verdict. Use before changing CUDA schedules, memory passes, kernels, streams, graphs, allocation, or module…
Quellsprache: Englisch
Design and review high-performance Metal compute, render, and hybrid architectures for Apple GPUs. Use when writing or reviewing Metal host code or MSL; choosing storage modes, resource lifetimes, synchronization, command-buffer structure, bindings, pipeline…
Quellsprache: Englisch
Isolate Zig code into a scratch harness and profile it with in-process hardware counters (instructions, cycles, IPC, energy), stack sampling, codegen summaries, and ABBA A/B comparison via the stwo-perf statistics. Use for any CPU performance question —…
Quellsprache: Englisch
Capture, sanitize, and attach the agent session transcripts that produced an optimization — the default part of every stwo-perf submission flow, and the most valuable dataset the harness curates. Use from the moment work begins (capture as you go, not after)…
Quellsprache: Englisch
Match algorithm-design work to canonical problems, computational models, complexity and parameterized results, published algorithms, and maintained implementations before coding. Use before creating or replacing algorithms for optimization, decision, search,…
Quellsprache: Englisch