galeos

While most of the focus here is understandably on the CPU side, there seems to be some interesting shifts taking place on the GPU side.

AMD currently has a process lead over Nvidia (and this is rumoured to be set to continue for a little while longer - apparently the first consumer Ampere chips are being fabbed on Samsung's inferior 8nm process due to lack of capacity at TSMC for the next few months)

Nvidia has clearly had an architecture advantage, although RDNA2 may close this gap, depending on how Ampere performs.

While Nvidia has had a much stronger showing in the GPGPU space, with CUDA helping it be the clear current winner, this also appears to have driven architecture decisions at Nvidia with the focus on tensor cores.

In gaming, Nvidia has put a lot of work into utilising these tensor cores for Deep Learning Super Sampling (DLSS). The idea being that you render at a lower resolution and then use deep learning to upscale in real-time to higher resolutions. DLSS 2.0 made some leaps in quality and DLSS 3.0 is on the horizon. It will be interesting to see:

a) How well they can get this working b) Is AMD working on its own version of this? c) If so, how well will the RDNA architecture be suited to this approach?

Will be interesting to watch how this plays out!

This obviously puts more wind in AMD's sails. That said, Intel's Datacenter growth is very impressive.

I have been an AMD investor since pre-Zen launch and I think it is fair to say that EPYC sales feel like they haven't reached the levels some have hoped for quite yet. If AMD continue to widen the gap between EPYC and Xeon, I suspect we may look back on this inertia as a bit of a 'roadrunner effect' and in a year or two's time get a shocking quarter where AMD suddenly starts taking surprisingly large bites out of Intel's datacenter market share.

Also on the GPU side of things, it is rumored that Nvidia has struggled to get capacity at TSMC and is having to use Samsung's, supposedly inferior, 8nm process for its forthcoming Ampere cards, at least until their 7nm capacity comes online at TSMC. Not sure how much truth there is to this, but highlights the capacity issues that may currently be in play.

MATLAB (which I think uses Intel MKL) showing faster performance for Intel than AMD but apparently due the above issues of the MKL libraries not utilising AVX on AMD processors? Is this correct?

https://www.extremetech.com/computing/302546-amd-threadrippe...

Curse was still very good - Murray the demonic talking skull was hilarious and the art and sound design was great.

I used to get these books out of the library when I was about 8 years old. Unfortunately the code often wouldn't work as I was attempting to use the BASIC interpreter on my IBM XT. I never understood why but those PEEK and POKEs can't have helped...

I can recommend 'Who is Fourier?' By the Transnational College of Lex. It assumes very little prior knowledge and introduces the reader to, among others, the concepts of trigonometry, calculus, imaginary numbers, logarithms and Fourier analysis.

I wish I'd known about this book when I was studying maths at school.

This is a multi-chip-module (MCM). Are the high core-count Xeons now all single die? Will be interesting to see what impact the MCM approach has on benchmarks as I supposed could have a latency impact in certain use cases?

As this blog post mentions, you can still make use of CUDA in Matlab without the Parallel Computing toolbox.

If you're interested in using CUDA via C-MEX in Matlab then I recommend the book 'Accelerating MATLAB with GPU Computing' by Jung W. Suh.

YouNeedABudget (YNAB) works for me as an effective budgeting tool where other solutions failed. It's approach acknowledges that you will over/underspend in particular spending categories. Highly recommended.

I can recommend 'Who is Fourier?' By the Transnational College of Lex. It assumes very little prior knowledge and introduces the reader to, among others, the concepts of trigonometry, calculus, imaginary numbers, logarithms and Fourier analysis.

I wish I'd known about this book when I was studying maths at school.