…Use virtualization to isolate the sandbox kernel from the host kernel (e.g., microVM, Kata container, full VM) Require user approval for every instance of specific actions (e.g., a network connection…
…CUDA C++ Apply an ACF to your kernel at compile time using the --apply-controls flag with NVCC 13.3+. Ideal for teams benchmarking with nvcc-compiled kernels. OpenAI Triton Pass an…
…independent of the relative timing of thread execution, the same inputs, kernel configuration, and GPU yield the same bitwise result. GPU-to-GPU determinism For applications that require the highest level of…
…SM copies The test launches a kernel copy where alternating thread warps are copying data in alternating directions. Multi-node operation Running NVbandwidth in multi-node mode requires additional setup and configuration…
…SWIOTLB tuning For specific use cases or non-standard peripherals requiring SWIOTLB—or when kernel logs indicate DMA issues—the reservation size can be adjusted using boot arguments. The swiotlb= parameter defines…
…The manual path requires engineers to rewrite the model (adding KV cache logic, attention kernels, sharding, kernel fusion, and more) before running it through the same runtime. Graph capture and pattern matching…
…Understanding and Optimizing Memory-Bound Kernels with Nsight Compute Learn how you can get the most out of Nsight Compute to identify and solve memory access inefficiencies in your kernel code. This…
…The same savings carry over to GPU VRAM usage at inference time because smaller engines require less memory to load and run. On the right, the latency story is just as compelling…
…Across a suite of real kernels (vector add, softmax, Cholesky, attention, Black-Scholes, FFT, matmul), warm JIT compile times are ~1.4x faster on geomean and up to ~2x faster on individual…
…The GPU kernel code is set up to run a reduction and then print out the time, and the objective function (not listed here) essentially builds and runs the kernel, and searches…