Develop High-Performance GPU Kernels in C++ with NVIDIA CUDA Tile | NVIDIA Technical Blog
… First, for best performance, the input and output arrays should only be accessed through their respective pointers while the kernel is running. …
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How does Slurm block scheduling optimize performance?
An important subtlety that often surprises users is the fact that Slurm can assign multiple segments of the same job to the same block. Using segments is essential for optimizing performance based on the specific locality requirements of the workload: Tensor Parallelism (TP) may require small, tight segments to keep latency-sensitive communication on the high-speed NVLink fabric, while Expert Parallelism (EP) may require larger segment sizes to enforce that all-to-all collective operations will always be performed within a single NVLink domain. Using a large segment value such as --segment=16
Achieving Peak System and Workload Efficiency on NVIDIA GB200 NVL72 with Slurm Block Scheduling | NVIDIA Technical BlogRunning AI Workloads on Rack-Scale Supercomputers: From Hardware to Topology-Aware Scheduling | NVIDIA Technical Blog
… By doing this, ComputeDomains make the high-performance fabric first-class in scheduling . …
Maximize AI Infrastructure Throughput by Consolidating Underutilized GPU Workloads | NVIDIA Technical Blog
… One workload can’t impact the performance or memory stability of another. …
NVIDIA CUDA 13.3 Enhances GPU Development with Tile Programming in C++, Compiler Autotuning, and Python Updates | NVIDIA Technical Blog
… PCG64 is the default PRNG in Numpy and provides a good balance between quality and performance. include include global void sample kernel { cuda::pcg64 rng threadIdx.x ; cuda::std::normal distribution dist 0.0f, 1.0f ; float sample = dist rng ; } Search: cub::DeviceFind::FindIf CCCL 3.3 adds cub::D… …
Achieving Peak System and Workload Efficiency on NVIDIA GB200 NVL72 with Slurm Block Scheduling | NVIDIA Technical Blog
… For example, to define two GB200 NVL72 domains use the following script: --- - topology: gb200-nvl72 cluster default: true block: block sizes: - 18 blocks: - block: block01 nodes: node 0001-0018 - block: block02 nodes: node 0019-0036 The Slurm topology/block plugin supports multiple levels of hiera… …
Advancing Emerging Optimizers for Accelerated LLM Training with NVIDIA Megatron | NVIDIA Technical Blog
… Check out the Megatron Bridge performance recipes . …
Scaling the AI-Ready Data Center with NVIDIA RTX PRO 4500 Blackwell Server Edition and NVIDIA vGPU 20 | NVIDIA Technical Blog
… To do this, first ensure the VM is powered off. …
Controlling Floating-Point Determinism in NVIDIA CCCL | NVIDIA Technical Blog
… Determinism performance comparison The level of determinism selected affects the performance of cub::DeviceReduce . Not-guaranteed determinism, with its relaxed requirements, provides the highest performance. …
Accelerating Long-Context Model Training in JAX and XLA | NVIDIA Technical Blog
… Communication backend comparison Each configuration was evaluated with two communication backends: NCCL baseline NVSHMEM-enabled implementation Measurements: TFLOP/s per device : GPU computational throughput Step time seconds : Time per training iteration Speedup : Relative performance improvement … …
Removing the Guesswork from Disaggregated Serving | NVIDIA Technical Blog
… HiSim also aids HiCache architecture exploration and cost/performance optimization through three-level KV cache design e.g., L2 size, prefetch/eviction policy, L3 bandwidth needs, write-through vs write-back to find the best cost–performance point. …