Posts tagged Installation

Introducing the AMD ROCm™ Offline Installer Creator: Simplifying Deployment for AI and HPC

Document headings start at H2, not H1 [myst.header]

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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

Goal: The machine learning ecosystem is quickly exploding and we aim to make porting to AMD GPUs simple with this series of machine learning blogposts.

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GPU-aware MPI with ROCm

MPI is the de facto standard for inter-process communication in High-Performance Computing. MPI processes compute on their local data while extensively communicating with each other. This enables MPI programs to be executed on systems with a distributed memory space e.g. clusters. There are different types of communications supported in MPI including point-to-point and collective communications. Point-to-point communication is the basic communication mechanism in which both the sending process and the receiving process take part in the communication. The sender has a buffer that holds the message and an envelope containing information that will be used by the receiver side (e.g., message tag, the sender rank number, etc.). The receiver uses the information in the envelope to select the specified message and stores it in its receiver buffer. In collective communication, messages can be exchanged among a group of processes rather than just two of them. Collective communication provides opportunities for processes to perform one-to-many and many-to-many communications in a convenient, portable and optimized way. Some examples of collective communications include broadcast, allgather, alltoall, and allreduce.

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AMD ROCm™ installation

AMD ROCm™ is the first open-source software development platform for HPC/Hyperscale-class GPU computing. AMD ROCm™ brings the UNIX philosophy of choice, minimalism and modular software development to GPU computing. Please see the AMD Open Software Platform for GPU Compute and ROCm Informational Portal pages for more information.

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