As the demand for high-performance computing (HPC) and AI applications grows, so does the importance of energy efficiency. NVIDIA Principal Developer Technology Engineer, Alan Gray, shares insights on optimizing energy and power efficiency for various applications running on the latest NVIDIA technologies, including NVIDIA H100 Tensor Core GPUs and NVIDIA DGX A100 systems. Traditionally��
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GPUs continue to get faster with each new generation, and it is often the case that each activity on the GPU (such as a kernel or memory copy) completes very quickly. In the past, each activity had to be separately scheduled (launched) by the CPU, and associated overheads could accumulate to become a performance bottleneck. The CUDA Graphs facility addresses this problem by enabling multiple GPU��
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GROMACS, a scientific software package widely used for simulating biomolecular systems, plays a crucial role in comprehending important biological processes important for disease prevention and treatment. GROMACS can use multiple GPUs in parallel to run each simulation as quickly as possible. Over the past several years, NVIDIA and the core GROMACS developers have collaborated on a series of��
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GROMACS, a simulation package for biomolecular systems, is one of the most highly used scientific software applications worldwide, and a key tool in understanding important biological processes including those underlying the current COVID-19 pandemic. In a previous post, we showcased recent optimizations, performed in collaboration with the core development team, that enable GROMACS to��
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GROMACS��one of the most widely used HPC applications�� has received a major upgrade with the release of GROMACS 2020. The new version includes exciting new performance improvements resulting from a long-term collaboration between NVIDIA and the core GROMACS developers. As a simulation package for biomolecular systems, GROMACS evolves particles using the Newtonian equations of motion.
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