GPU – Observations from Uppsala

“Unusual Perspectives on AI” – Computer and System Architecture Unraveled Event Five

The fifth CaSA, Computer and System Architecture Unraveled, meetup took place on January 30. We finally gave in and joined the AI hype train, resulting in an event with a somewhat different audience and different discussions. More society and applications, less computer architecture. Our two presenters were Håkan Zeffer from SambaNova Systems and Björn Forsberg from RI.SE (doing his second CaSA presentation!). Håkan talked about the architecture of the Sambanova AI processors, and Björn about AI compilers.

Hi Local AI, Draw Me …

I recently built a new desktop computer, featuring an Intel ARC 770 graphics card (just to be different). The card is supported by the Intel AI Playground, which is a software package that makes it dead easy to run AI/large language models (LLM) locally on my GPU. I was curious as to just what this could do, as compared to the big AI models that run on cloud servers.

GPGPU for Instruction-Set Simulation – Maybe, Maybe not

I just read a quite interesting article by Christian Pinto et al, “GPGPU-Accelerated Parallel and Fast Simulation of Thousand-core Platforms“, published at the CCGRID 2011 conference. It discusses some work in using a GPGPU to run simulations of massively parallel computers, using the parallelism of the GPU to speed the simulation. Intriguing concept, but the execution is not without its flaws and it is unclear at least from the paper just how well this generalizes, scales, or compares to parallel simulation on a general-purpose multicore machine.

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GPU Programming: a Good Pattern to Follow?

In the March/April 2008 issue of ACM Queue, there is an article on GPU Programming by Kayvon Fatahalian and Mike Houston of Stanford that I found a very interesting read. It presents and analyzes the programming model of modern GPUs, in the most coherent and understandable way that I have seen so far. The PC GPU has a model for programming parallel hardware that might be a good pattern for other areas of processing. Programmers do not have to write explicitly parallel code, the machinery and hardware takes care of ensuring parallel behavior, as long as the code follows the assumptions made in the model.

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Heterogeneous vs homogeneous systems, revisited

I got another email from my friend with the thesis that processors will become ever more homogeneous as time goes on, while I believe in a relative heterogenezation (is that a word?) of computer architecture with many special-purpose accelerators and helper processors. This argument is put forward in a previous blog post. In this round, the arguments for homogenization are from the gaming world.

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