Gráfica Intel Arc Discrete GPUs

Intel Arc Graphics A580 On Linux: Open-Source Graphics For Under $200​

ASRock Challenge Arc A580 8GB graphics card that is being used for all of this initial testing of Arc Graphics A580 under Linux. There is also the SPARKLE Intel Arc A580 ORC OC Edition that launched last week as well and coming in at $179 USD. As of writing this article days after launch, both models continue to be available from major Internet retailers for around $179 USD.
For the purposes of this benchmarking all tests were done on Linux 6.6-rc5 and Mesa 23.3-devel but using older components is fine too.
In this initial A580 article is a look at the Arc Graphics A380 / A680 / A750 / A770 across a wide range of Linux gaming tests as well as GPU compute benchmarks using OpenCL and oneAPI/SYCL. A number of the new oneAPI tests are added to make things interesting.
Screenshot-2023-10-17-at-18-39-24-Intel-Arc-Graphics-A580-On-Linux-Open-Source-Graphics-For-Under-2.png


On Linux 6.6 + Mesa 23.3-devel, the Arc Graphics A580 was running at 93% the speed of the Arc Graphics A750 or 89% the speed of the flagship Arc Graphics A770.
https://www.phoronix.com/review/intel-arc-graphics-a580

Ainda vai sair um a comparar vs AMD vs Nvidia
 

Preparing a legacy event-generator code for GPU​


As part of an ongoing Aurora Early Science Program (ESP) project to prepare the ATLAS experiment at CERN’s Large Hadron Collider (LHC) for the exascale era of computing—“Simulating and Learning in the ATLAS Detector at the Exascale,” led by Walter Hopkins— researchers are porting and optimizing the codes that will enable the experiment to run its simulation and data analysis tasks on an array of next-generation architectures. Among them is the soon-to-launch Intel-HPE Aurora system housed at the Argonne Leadership Computer Facility (ALCF), a U.S. Department of Energy (DOE) Office of Science user facility at Argonne National Laboratory.
One such code is MadGraph, a particle interaction simulator for LHC experiments that performs particle-physics calculations to generate expected LHC-detector particle interactions. As a framework, MadGraph aims at a complete Standard Model and Beyond Standard Model phenomenology, including such elements as cross-section computations as well as event manipulation and analysis.

Defining performance portability for MadGraph​

“To me, to be portable and performant means that the application needs to be capable of running efficiently and effectively on as many devices as possible—irrespective of vendor, whether it’s NVIDIA or Intel or AMD,” Nichols said. “If an application runs really well on Intel GPUs but has problems running on NVIDIA GPUs, it’s not really performance portable. As a developer you also want the code to be easily maintainable, so you don’t want to have a patchwork of different chunks of code as your code base—you want to write one code and have that code be performant on all different devices.”

Determining which portability framework to adopt​

The team had already settled on testing three portability frameworks, SYCL, Kokkos, and alpaka, when Nichols joined the project. He would take the lead on developing the SYCL version.

Five representative physics processes were chosen as standard cases to test code performance.

When it became time to work in earnest on the SYCL port of MadGraph, Nichols’s first step was to examine the native CUDA code to identify areas in which performance gains could be made.
Using GitLab, Nichols set up continuation integration software pipelines in order to carry out regular performance tests on the various devices hosted at Argonne’s Joint Laboratory for System Evaluation (JLSE). The systems on which these nightly performance tests ran included Nvidia GPUs (V100 and A100), Intel GPUs (early versions of those in Aurora), Intel CPUs (Skylake), and AMD GPUs (MI50, MI100, MI250).
He conducted performance scaling to see which portability framework delivered the best performance across the different GPUs. Outperforming even the native CUDA and CPU codes, it was eventually determined that the SYCL port was the most performant on all tested systems, with Kokkos a close second. Given these metrics, the ATLAS team chose to move forward with SYCL and discontinue development with the other portability frameworks.
https://www.alcf.anl.gov/news/preparing-legacy-event-generator-code-gpu
 
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