Deep learning and
artificial intelligence
have been huge topics of interest in 2016, but so far most of the
excitement has focused on either Nvidia GPUs or custom silicon hardware
like Google’s TensorFlow. We know Intel is working on upcoming Xeon
Phi-class silicon to throw at these problems, and AMD wants to enter the
market too, courtesy of a new lineup of graphics cards based on three
different product families. AMD will also offer its own software tools
and customized libraries to accelerate these workloads. It’s still
fairly early days for the AI and deep learning markets, and AMD could
definitely use the cash — but what’s it going to bring to the table?
First up, let’s talk about the accelerators
themselves. AMD is deploying three new cards under its new Radeon
Instinct brand, from three different product families:
The MI6 is derived from Polaris, albeit
Polaris
running at a slightly lower clock than the boost frequencies we saw on
consumer parts (total onboard RAM, however, is 16GB). The MI8 is a
smaller GPU built around R9 Nano and clocked at the same frequencies,
with the same 4GB RAM limitation. (It’s not clear how much AI and deep
learning workloads depend on RAM, but AMD presumably wouldn’t sell the
chip into this market if it didn’t have a viable use-case for it.
Finally, the MI25 will be a Vega-derived chip that’s expected to be
significantly faster than the other two cards, but AMD isn’t giving any
details or information on that core yet. AMD hasn’t specified a ship
date for any of these products beyond H1 2017, but we’d expect the
company to bring its MI6 and MI8 cards out first, to test the waters and
establish a foothold in the market.
It might seem crazy to think that AMD would
seek to compete against Nvidia with older and midrange consumer
hardware, but it’s probably a smart move. Nvidia still sells a range of
HPC products based on Maxwell and Kepler hardware, and AMD’s GCN was
actually a very strong competitor against Nvidia in a number of compute
workloads. Toss in the fact that AMD continues to offer a CUDA
compatibility layer, and Team Red has a plausible argument for its own
hardware, at least if it brings pricing in appropriately (and in the HPC
world, “appropriately” can still be plenty profitable). The question,
however, is how many resources AMD will be able to dedicate to the
software side of this particular equation, and whether it can overcome
Nvidia’s near-decade lead in GPGPU computing.
Of all the reasons we’ve heard for why Nvidia
took such a leadership position in HPC and scientific computing, one of
the most consistent has nothing to do with hardware comparisons. AMD
held a leadership position in multiple compute benchmarks and workloads
during the Kepler and Maxwell eras, often by enormous margins (this is
part of why AMD GPU prices spiked in 2013-2014). OpenCL, however, wasn’t
really in a state to capitalize on the strength of AMD’s underlying
hardware, and
AMD
didn’t have the resources to spend on a major bring-up or enterprise
computing push. Since then, we’ve seen incremental progress on this
front, with last years’ Boltzmann initiative, various server and
virtualization product launches, and now the Radeon Instinct brand.
Radeon Instinct products will use AMD’s MIOpen GPU accelerated library
to “to provide GPU-tuned implementations for standard routines such as
convolution, pooling, activation functions, normalization and tensor
format” while its ROCm deep learning network “is also now optimized for
acceleration of popular deep learning frameworks, including Caffe, Torch
7, and Tensorflow, allowing programmers to focus on training neural
networks rather than low-level performance tuning through ROCm’s rich
integrations. ROCm is intended to serve as the foundation of the next
evolution of machine intelligence problem sets, with domain-specific
compilers for linear algebra and tensors and an open compiler and
language runtime.”
AMD is also partnering with some hardware
customers to build custom Zen systems for server rack deployments with
varying numbers of accelerator cards in them, but obviously this
hardware won’t be available for quite some time, since Zen’s server
launch isn’t expected until Q2 2017. We expect to see both Zen and Vega
in consumer hardware first, before launching for server.
It’s good to see AMD pushing for markets where
its graphics cards might be particularly well-suited, given GCN’s
historic compute strengths, but it’s not clear if it’ll be able to
muster the software expertise to win market share.
Nvidia
has been plugging away at this for nearly ten years and Intel has
boatloads of cash to throw at the problem. Between those two companies,
there may not be much room for AMD at the proverbial table. While AMD
took pains to call out its expertise in heterogeneous computing and
implied this could give it a leg up once Zen is shipping, that’s a very
tenuous argument right now. Nearly three years after Kaveri launched,
I’m not aware of any significant software with HSA support, and AMD’s
presence in the GPGPU market is anemic at best. Easy-to-use tools and
compatibility with both OpenCL and CUDA could change that going forward,
but this is a long-term play. It’ll take a few more years before we can
fairly gauge whether it’s a success.
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