![]() ![]() The combination of NVIDIA Quantum-2 InfiniBand networking, NVLink, NVSwitch, and the Magnum IO software stack delivers out-of-the-box scalability for hundreds to thousands of GPUs operating together. GDS brings up to 7.2X the performance increase in deep learning inference with DALI compared to baseline Numpy.Įnabling researchers to continue pushing the envelope of what's possible with AI requires powerful performance and massive scalability. Magnum IO GPUDirect Storage (GDS) has been enabled in the Data Loading Library (DALI) through the Numpy reader operator. The benefits of NCCL include faster time to model training accuracy, while achieving close to 100 percent interconnect bandwidth between servers in a distributed environment. NCCL and other Magnum IO libraries transparently leverage the latest NVIDIA H100 GPU, NVLink, NVSwitch, and InfiniBand networks to provide significant speedups for deep learning workloads, particularly recommender systems and large language model training. Magnum IO Libraries and Deep Learning Integrations This allows researchers to train massive models at the speed of business. The combination of NVIDIA NVLink, NVIDIA NVSwitch, NVIDIA Magnum IO libraries and strong scaling across servers delivers AI training speedups of up to 9X on Mixture of Experts (MoE) models. Compressing this to the speed of business to complete training within days requires high speed, seamless communication between every GPU in a server cluster, so they can scale performance. The emerging class of exascale HPC and trillion parameter AI models for tasks like superhuman conversational AI require months to train, even on supercomputers. Largest Interactive Volume Visualization - 150TB NASA Mars Lander Simulation Multi-Node Multi-GPU: Using NVIDIA cuFFTMp FFTs at Scale This reduces latencies and improves strong scaling. The Qualitative Data Analysis (QUDA) Lattice Quantum Chromodynamics library can use NVSHMEM for communication to reduce overheads from CPU and GPU synchronization, and improve compute and communication overlap. By using NVIDIA Shared Memory Library (NVSHMEM)™, cuFFTMp is independent of the MPI implementation and operates closer to the speed of light, which is critical as performance can vary significantly from one MPI to another. ![]() NCCL, UCX, and HPC-X are all part of the HPC-SDK.įast Fourier Transforms (FFTs) are widely used in a variety of fields, ranging from molecular dynamics, signal processing, and computational fluid dynamics (CFD) to wireless multimedia and ML applications. NVIDIA HPC-X increases CPU availability, application scalability, and system efficiency for improved application performance, which is distributed by various HPC ISVs. ![]() UCX accelerates scientific computing applications, such as VASP, Chroma, MIA-AI, Fun3d, CP2K, and Spec-HPC2021, for faster wall-clock run times. VASP performance improves significantly when MPI is replaced with NCCL. This delivers optimal results, as well as the most efficient HPC and ML deployments at any scale. Magnum IO, on the latest NVIDIA Quantum-2 InfiniBand platform, features new and improved capabilities for mitigating the negative impact on a user’s performance. In multi-tenant environments, user applications may be unaware of indiscriminate interference from neighboring application traffic. ![]() Magnum IO exposes hardware-level acceleration engines and smart offloads, such as RDMA, NVIDIA GPUDirect, and NVIDIA SHARP, while bolstering the high bandwidth and ultra-low latency of NVIDIA InfiniBand and NVIDIA NVLink networked GPUs. Leading simulation and applications leverage NVIDIA Magnum IO to enable faster time to insight. To unlock next-generation discoveries, scientists rely on simulation to better understand complex molecules for drug discovery, physics for new sources of energy, and atmospheric data to better predict extreme weather patterns. Accelerated Computing for Enterprise IT. ![]()
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