Re: Interacting with coherent memory on external devices

[Oded Gabbay]

On 04/23/2015 07:22 PM, Jerome Glisse wrote:
> On Thu, Apr 23, 2015 at 09:20:55AM -0500, Christoph Lameter wrote:
> > On Thu, 23 Apr 2015, Benjamin Herrenschmidt wrote:
> >
> > > > There are hooks in glibc where you can replace the memory
> > > > management of the apps if you want that.
> > >
> > > We don't control the app. Let's say we are doing a plugin for libfoo
> > > which accelerates "foo" using GPUs.
> >
> > There are numerous examples of malloc implementation that can be used for
> > apps without modifying the app.
>
> What about share memory pass btw process ? Or mmaped file ? Or
> a library that is loaded through dlopen and thus had no way to
> control any allocation that happen before it became active ?
>
> > > Now some other app we have no control on uses libfoo. So pointers
> > > already allocated/mapped, possibly a long time ago, will hit libfoo (or
> > > the plugin) and we need GPUs to churn on the data.
> >
> > IF the GPU would need to suspend one of its computation thread to wait on
> > a mapping to be established on demand or so then it looks like the
> > performance of the parallel threads on a GPU will be significantly
> > compromised. You would want to do the transfer explicitly in some fashion
> > that meshes with the concurrent calculation in the GPU. You do not want
> > stalls while GPU number crunching is ongoing.
>
> You do not understand how GPU works. GPU have a pools of thread, and they
> always try to have the pool as big as possible so that when a group of
> thread is waiting for some memory access, there are others thread ready
> to perform some operation. GPU are about hidding memory latency that's
> what they are good at. But they only achieve that when they have more
> thread in flight than compute unit. The whole thread scheduling is done
> by hardware and barely control by the device driver.
>
> So no having the GPU wait for a page fault is not as dramatic as you
> think. If you use GPU as they are intended to use you might even never
> notice the pagefault and reach close to the theoritical throughput of
> the GPU nonetheless.
>
>
> > > The point I'm making is you are arguing against a usage model which has
> > > been repeatedly asked for by large amounts of customer (after all that's
> > > also why HMM exists).
> >
> > I am still not clear what is the use case for this would be. Who is asking
> > for this?
>
> Everyone but you ? OpenCL 2.0 specific request it and have several level
> of support about transparent address space. The lowest one is the one
> implemented today in which application needs to use a special memory
> allocator.
>
> The most advance one imply integration with the kernel in which any
> memory (mmaped file, share memory or anonymous memory) can be use by
> the GPU and does not need to come from a special allocator.
>
> Everyone in the industry is moving toward the most advance one. That
> is the raison d'être of HMM, to provide this functionality on hw
> platform that do not have things such as CAPI. Which is x86/arm.
>
> So use case is all application using OpenCL or Cuda. So pretty much
> everyone doing GPGPU wants this. I dunno how you can't see that.
> Share address space is so much easier. Believe it or not most coders
> do not have deep knowledge of how things work and if you can remove
> the complexity of different memory allocation and different address
> space from them they will be happy.
>
> Cheers,
> Jérôme
I second what Jerome said, and add that one of the key features of HSA 
is the ptr-is-a-ptr scheme, where the applications do *not* need to 
handle different address spaces. Instead, all the memory is seen as a 
unified address space.

See slide 6 on the following presentation:
http://www.slideshare.net/hsafoundation/hsa-overview

Thanks,
	Oded
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