ENGINEERING COMPUTING ||| Workstations
30 DE^ | Technology for Optimal Engineering Design August 2019 /// DigitalEngineering247.com
“Data science is one of the fastest growing fields of com-
puter science and impacts every industry,” said NVIDIA
founder and CEO Jensen Huang at the announcement. “En-
terprises are eager to unlock the value of their business data
using machine learning and are hiring—at an unprecedented
rate—data scientists who require powerful workstations ar-
chitected specifically for their needs.”
An obvious question comes to mind: When are specialized
workstations needed? Why not just go with the utility of a
typical professional workstation? Digital Engineering asked
NVIDIA and several workstation vendors.
Their answers repeated a general theme, one we have
been hearing from several sources, not just workstation ven-
dors: There is strong and increasing demand for data science
in every industry that now uses professional workstations.
There are not enough data science specialists to meet em-
ployer demand, so engineers and programmers from other
disciplines are being drafted as data scientists. This lack of
expertise extends to the specifics of what software to run and
what computer hardware is best suited for the task.Specification Defined
A new workstation meeting NVIDIA’s standard will have sev-
eral specific features not common to most existing worksta-
tions. The first is dual NVIDIA Quadro RTX graphics pro-
cessing units (GPUs), based on the Turing GPU architecture.
Each Quadro RTX offers up to 96GB of fast local memory,
required for large data sets typical of artificial intelligence(AI) training or deep learning and machine learning analysis.
The new NVIDIA GV100, a Volta class GPU, also may be
used in a data science workstation.
Both the RTX line and the GV100 use two new types of
compute cores, RT cores and Tensor cores. RT is short for
ray tracing but could also refer to real time; these cores are
specialized for high-performance, local visualization.
Tensor Cores specialize in matrix math, common to deep
learning and some applications in other fields that now run
only on high-performance computing (HPC) clusters or
cloud computing platforms. “[Tensor Cores] do the basics for
workhorse calculating in deep learning,” says Michael Hous-
ton, a senior distinguished engineer at NVIDIA.
Tensor cores perform a fused multiply add, where two 4x4
FP16 matrices are multiplied, and the result added to a 4x4
FP16 or FP32 matrix. It sounds like high school math, but
tensor cores do millions of these calculations every second,
much faster than commodity CPU or GPU compute cir-
cuitry. There is also an advantage in the tensor core’s ability
to accumulate everything in FP32. “Thirty-two bit accumula-
tion tends to really matter for convergence of networks,” says
Houston, “to make mixed precision really work.” Houston
says the theoretical performance boost of using tensor cores
is 8x. “On a lot of neural nets, NVIDIA sees a 4x speed in-
crease end to end.” Data science models often take several
days to run; a 4x speed increase would complete a four-day
job in one day.
The NVIDIA Data Science Workstation specification calls
for Ubuntu Linux 18.04, nicknamed Bionic Beaver, as the
operating system. Along with Ubuntu comes a set of software
libraries based on the NVIDIA CUDA-X AI protocol for AI
research. The collection includes RAPIDS (rapids.ai), Tensor-
Flow (tensorflow.org), PyTorch (pytorch.org) and Caffe
(caffe.berkeleyvision.org) open source libraries and several
NVIDIA-written acceleration libraries for machine learn-
ing, artificial intelligence and deep learning.The Rise of
Data Science Workstations
NVIDIA’s new hardware is making it easier for organizations to process data
right on the desktop, as engineers are being drafted into data science roles.BY RANDALL S. NEWTONE
ARLIER THIS YEAR, NVIDIA announced
a reference architecture for a new class of
professional workstation, the data science
workstation. Almost immediately, leading
workstation original equipment manufacturers (OEMs)
announced workstations that conform to NVIDIA’s Data
Science specification.DE_0819_Specialized_Workstations_Newton.indd 30 7/11/19 11:14 AM