AMD has recently released version 4.0 of their AOCC compiler which includes support for AVX512 on the Zen4 architecture. This post details building a Docker image containing the Spack package manager/build system together with AMD AOCCv4.0.0 compilers. This will be used as the build image for multi-stage Dockerfiles that will be used to compile scientific applications and benchmarks with targeted Zen3/4 optimizations. It is the first step in that process.
This post presents preliminary ML-AI and Scientific application performance results comparing NVIDIA RTX 4090 and RTX 3090 GPUs. These are early results using the NVIDIA CUDA 11.8 driver.
This post presents scientific application performance testing on the new AMD Ryzen 7950X. I am impressed! Seven applications that are heavy parallel numerical compute workloads were tested. The 7950X outperformed the Ryzen 5950X by as much as 25-40%. For some of the applications it provided nearly 50% of the performance of the much larger and more expensive Threadripper Pro 5995WX 64-core processor. That’s remarkable for a $700 CPU! The Ryzen 7950X is not in the same platform class as the Tr Pro but it is a respectable, budget friendly, numerical computing processor.
We’ve been curious about the performance of WSL for scientific applications and decided to do a few relevant benchmarks. This is also a teaser for some hardware-specific optimized application containerization that I’ve been working on!
The single socket version of Intel third generation Xeon SP is out, the Ice Lake Xeon W 33xx. This is a much better platform with faster large capacity 8 channel memory and PCIe v4 with plenty of lanes. The new Intel platform is very much like the AMD Threadripper Pro (single socket version of EPYC Rome) so this is the obvious comparison to make. Read on to see how the numerical computing testing went!
NVIDIA Enroot has a unique feature that will let you easily create an executable, self-contained, single-file package with a container image AND the runtime to start it up! This allows creation of a container package that will run itself on a system with or without Enroot installed on it! “Enroot Bundles”.
For computing tasks like Machine Learning and some Scientific computing the RTX3080TI is an alternative to the RTX3090 when the 12GB of GDDR6X is sufficient. (Compared to the 24GB available of the RTX3090). 12GB is in line with former NVIDIA GPUs that were “work horses” for ML/AI like the wonderful 2080Ti.
The NVIDIA A100 (Compute) GPU is an extraordinary computing device. It’s not just for ML/AI types of workloads. General scientific computing tasks requiring high performance numerical linear algebra run exceptionally well on the A100.
Enroot is a simple and modern way to run “docker” or OCI containers. It provides an unprivileged user “sandbox” that integrates easily with a “normal” end user workflow. I like it for running development environments and especially for running NVIDIA NGC containers. In this post I’ll go through steps for installing enroot and some simple usage examples including running NVIDIA NGC containers.
The new Intel Rocket Lake CPUs have been officially released. There were numerous posts and reviews before the official release date of March 30 2021, but I haven’t seen anything about the numerical compute performance. I’ve had access to a Core-i9 11900KF 8-core CPU and have compared it with (my own) AMD 5800X system.
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