The GitHub for performance engineering.
We reinvented performance engineering, scalable for everyone. Every new model feature or chip you adopt breaks yesterday's optimizations, so keeping your software fast means redoing the same work by hand, over and over. We make it automatic, so your performance never falls behind.
Measure performance on every commit.
Every push carries your workloads straight from GitHub into the performance dashboard of our Cloud Console. We run them on real hardware, surface the hotspots, and place each part of your code on the roofline of the hardware, so you see where time goes and what limits it.
daisytuner/modelsmain · 6d6dbef
Performance dashboard2 workloads · todayLivepytorch_models2 workloadstorch—78.0 ms+3.0%torch-cuda—17.9 ms−7.5%docc-cuda93 regions11.2 ms1.60×+0.0%docc-omp45 regions41.5 ms1.88×−1.8%torch-cuda—36.4 ms−3.4%docc-cuda266 regions21.7 ms1.68×+0.6%Performance optimizations, reviewed like code.
Our compiler, docc, represents every code as a dataflow IR that makes data movement, parallelism and computation explicit. In the web IDE of our Cloud Console you compose optimization recipes yourself. The compiler checks each recipe for legality, and you're ready to open the result as a pull request.
[segformer] fused gemm and div
MergedServerCUDAA live database, queried by docc.
Your performance database stores every optimization recipe, and docc matches them against your code automatically during compilation. Purpose-built performance embeddings find similar regions and the ideal optimization for yours. A RAG method for compilers.
segformer.fused_gemm_divpull request · mergedCUDAinterchange ×2 · CUDA(32) · nested(8, 4)polyhedral · proven2.14×9%3 · transfer-tunableAgents feed your database, day and night.
Let our agents tune your code for you. They search for optimizations around the clock, propose recipes, and the compiler proves each one correct before it joins the database. Every proven recipe makes the next search faster, so the database keeps improving on its own.
attention.softmax_matmulServerCUDA2.6×2m agogemm.bf16ServerRocm1.9×5m agolayernorm.fusedServerOpenMP—12m agoconv2d.3x3ServerSequential1.4×31m ago