Data Science Blogs - Page 2#
Introducing ROCm-LS: Accelerating Life Science Workloads with AMD Instinct™ GPUs
Accelerate life science and medical workloads with ROCm-LS, AMDs GPU-optimized toolkit for faster multidimensional image processing and vision.
Announcing hipCIM: A Cutting-Edge Solution for Accelerated Multidimensional Image Processing
Fully utilize the power of AMDs Instinct GPUs to process and interpret detailed multidimensional images with lightning speed.
Unlocking GPU-Accelerated Containers with the AMD Container Toolkit
Simplify GPU acceleration in containers with the AMD Container Toolkit—streamlined setup, runtime hooks, and full ROCm integration.
Introducing ROCm-DS: GPU-Accelerated Data Science for AMD Instinct™ GPUs
Accelerate data science with ROCm-DS: AMD’s GPU-optimized toolkit for faster data frames and graph analytics using hipDF and hipGRAPH
Accelerate DeepSeek-R1 Inference: Integrate AITER into SGLang
Boost DeepSeek-R1 with AITER: Step-by-step SGLang integration for high-performance MoE, GEMM, and attention ops on AMD GPUs
Accelerated JPEG decoding on AMD Instinct™ GPUs with rocJPEG
Learn how to decompress JPEG files at breakneck speeds for your AI, vision, and content delivery workloads using rocJPEG and AMD Instinct GPUs.
Optimizing DeepseekV3 Inference on SGLang Using ROCm Profiling Tools
Dive into kernel-level profiling of DeepseekV3 on SGLang—identify GPU bottlenecks and boost large language model performance using ROCm
Installing ROCm from source with Spack
Install ROCm and PyTorch from source using Spack. Learn how to optimize builds, manage dependencies, and streamline your GPU software stacks.
Understanding RCCL Bandwidth and xGMI Performance on AMD Instinct™ MI300X
The blog explains the reasons behind RCCL bandwidth limitations and xGMI performance constraints, and provides actionable steps to maximize link efficiency on AMD MI300X
Seismic stencil codes - part 2
Seismic Stencil Codes - Part 2: In the previous post, recall that the kernel with stencil computation in the z-direction suffered from low effective bandwidth. This low performance comes from generating substantial amounts of data to movement to global memory.
Seismic stencil codes - part 3
Seismic Stencil Codes - Part 3: In the last two blog posts, we developed a HIP kernel capable of computing high order finite differences commonly needed in seismic wave propagation.
Using statistical methods to reliably compare algorithm performance in large generative AI models with JAX Profiler on AMD GPUs
Using Statistical Methods to Reliably Compare Algorithm Performance in Large Generative AI Models with JAX Profiler on AMD GPUs