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Alan Gray

As seen in: NVIDIA Developer
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Articles

Optimizing llama.cpp AI Inference with CUDA Graphs

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By Alan Gray
| NVIDIA Developer
The open-source llama.cpp code base was originally released in 2023 as a lightweight but efficient framework for performing inference on Meta Llama models. Built on the GGML library released the previous year, llama.cpp quickly became attractive to many users and developers (particularly for use on personal workstations) due to its focus on C/C++ without the need for complex dependencies.
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A Guide to CUDA Graphs in GROMACS 2023

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By Alan Gray, Szilárd Páll
| NVIDIA Developer
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 activities to be scheduled as a single computational graph.
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Massively Improved Multi-node NVIDIA GPU Scalability with GROMACS

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By Alan Gray, Gaurav C. Garg, Szilárd Páll
| NVIDIA Developer
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 multi-GPU and multi-node optimizations.
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