[petsc-dev] Soliciting suggestions for linear solver work under SciDAC 4 Institutes

Jeff Hammond jeff.science at gmail.com
Thu Jul 7 19:06:44 CDT 2016

On Thu, Jul 7, 2016 at 4:34 PM, Richard Mills <richardtmills at gmail.com>

> On Fri, Jul 1, 2016 at 4:13 PM, Jeff Hammond <jeff.science at gmail.com>
> wrote:
>> [...]
>> Maybe I am just biased because I spend all of my time reading
>> www.nextplatform.com, but I hear machine learning is becoming an
>> important HPC workload.  While the most hyped efforts related to running
>> inaccurate - the technical term is half-precision - dense matrix
>> multiplication as fast as possible, I suspect that more elegant approaches
>> will prevail.  Presumably there is something that PETSc can do to enable
>> machine learning algorithms.  As most of the existing approaches use silly
>> programming models based on MapReduce, it can't be too hard for PETSc to do
>> better.
> "Machine learning" is definitely the hype du jour, but when that term gets
> thrown around, everyone is equating it with neural networks with a lot of
> layers ("deep learning").  That's why everyone is going on about half
> precision dense matrix multiplication, as low accuracy works fine for some
> of this stuff.  The thing is, there are a a ton of machine-learning
> approaches out there that are NOT neural networks, and I worry that
> everyone is too ready to jump into specialized hardware for neural nets
> when maybe there are better approaches out there.  Regarding machine
> learning approaches that use sparse matrix methods, I think that PETSc
> (plus SLEPc) provide pretty good building blocks right now for these,
> though there are probably things that could be better supported.  But what
> machine learning approaches PETSc should target right now, I don't know.
> Program managers currently like terms like "neuromorphic computing" and
> half-precision computations seem to be the focus.  (Though why stop there?
> Why not quarter precision?!!)
Google TPU does quarter precision i.e. 8-bit fixed-point [
so the machine learning folks have already gone there.  No need to
speculate about it :-)


Jeff Hammond
jeff.science at gmail.com
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