Posts tagged Installation

Graph analytics on AMD GPUs using Gunrock

Graphs and graph analytics are related concepts that can help us understand complex data and relationships. In this context, a graph is a mathematical model that represents entities (called nodes or vertices) and their connections (called edges or links). And graph analytics is a form of data analysis that uses graph structures and algorithms to reveal insights from the data.

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Application portability with HIP

Many scientific applications run on AMD-equipped computing platforms and supercomputers, including Frontier, the first Exascale system in the world. These applications, coming from a myriad of science domains, were ported to run on AMD GPUs using the Heterogeneous-compute Interface for Portability (HIP) abstraction layer. HIP enables these High-Performance Computing (HPC) facilities to transition their CUDA codes to run and take advantage of the latest AMD GPUs. The effort involved in porting these scientific applications varies from a few hours to a few weeks and largely depends on the complexity of the original source code. Figure 1 shows several examples of applications that have been ported and the corresponding porting effort.

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Creating a PyTorch/TensorFlow code environment on AMD GPUs

Note: This blog was previously part of the AMD lab notes blog series.

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GPU-aware MPI with ROCm

Note: This blog was previously part of the AMD lab notes blog series.

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AMD ROCm™ installation

Note: This blog was previously part of the AMD lab notes blog series.

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