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From: Linus Walleij <linus.walleij@linaro.org>
Date: Tue, 14 Sep 2021 15:07:50 +0200
Message-ID: <CACRpkdZ_xwCepDXx+2nywzmBqq0ey_5kC9e06m4+ef9k3JUHeQ@mail.gmail.com>
Subject: Re: [MAINTAINER SUMMIT] User-space requirements for accelerator drivers
To: Arnd Bergmann <arnd@arndb.de>
Cc: Dave Airlie <airlied@gmail.com>, Daniel Vetter <daniel.vetter@ffwll.ch>, Greg KH <greg@kroah.com>, 
	Leon Romanovsky <leon@kernel.org>, Laurent Pinchart <laurent.pinchart@ideasonboard.com>, 
	Thomas Gleixner <tglx@linutronix.de>, Josh Triplett <josh@joshtriplett.org>, 
	Mauro Carvalho Chehab <mchehab@kernel.org>, Jonathan Corbet <corbet@lwn.net>, ksummit@lists.linux.dev, 
	dev@tvm.apache.org
Content-Type: text/plain; charset="UTF-8"

On Mon, Sep 13, 2021 at 3:20 PM Arnd Bergmann <arnd@arndb.de> wrote:

> One straightforward hardware independent low-level API would
> be the traditional BLAS GEMM call[1] for matrix multiplication
> and its variants (integer, float, bfloat16, ...).

What this (and subsequent posts from Dave and Daniel) show, is
that the general pattern is that what we are accelerating is no longer
the specialized use cases of linear algebra such as 3D "shaders"
or whatever inference linear algebra NPUs are doing, which
appear to include regression, bayesian stuff, gaussian quadrature...
name it.

What we are talking about here is acceleration, using an efficient
data path, of numerical analysis, using tailored hardware.
I'm not even sure we are limited to linear algebra anymore.

Is this what is happening, and should we be thinking numerical
analysis accelerators and their different shapes and sizes
rather than usecase-foo-accelerators, so we don't end up with
this situation again the next time applied math comes knocking
on the door with their next usecase?

Yours,
Linus Walleij