IBM Systems Magazine, Power Systems - August 2018 - 11
bandwidth, tight integration with
GPU accelerators, and parallel
multithreaded IBM POWER*
architecture that is well adapted to
ensure that GPUs are used to their
fullest potential," Shah says.
MXNet is an Apache
open-source project for training
and deploying deep neural
networks, which touts scalability,
development speed, portability
and memory, and computation
efficiency. NXNet also automates
common workflows, making the
expression of standard neural
networks quick and concise.
The development team chose
MXNet.jl, coded in Julia, for the
deep learning work.
According to Shah, the POWER
processor with NVIDIA GPU
accelerators increased processing
speed by 57x and provides 2x-3x
more memory bandwidth. Those
attributes, combined with tight GPU
accelerator integration, creates a
high-performance environment for
deep learning with Julia.
In designing the POWER8
platform, IBM partnered with
NVIDIA to develop NVLink, a fast
communication path between
on-board CPUs and GPUs.
Computing performance is also
increased by the use of NVIDIA
Tesla P100 NVLink-enabled GPUs.
Given rapidly growing data
set sizes and the increasing
sophistication and complexity
of artificial intelligence systems,
hardware and its associated
infrastructure needs to scale
accordingly while remaining
cost-efficient, according to an IDC
And the OS is no longer an
obstacle: Linux* on POWER
allows, in most cases, for a
seamless transition with a
recompiler for compiled code.
An IDC whitepaper makes the
case that clients would be doing
themselves a disservice by
not comparing x86 price and
performance metrics with those
of the Power Systems platform
The world is rapidly changing.
Challenges are greater than ever,
and so are clients' choices for how
to tackle them.
ibmsystemsmag.com AUGUST 2018 // 11