Check out weekly insights into the world of HPC and AI with this HPC Top 5 Stories.

1. HPC TOP 5 STORIES
Weekly Insights into the World of
High Performance Computing

2. HPC AND AI HAVE PAVED THE WAY FOR
GROUNDBREAKING DISCOVERIES IN SCIENCE,
MEDICINE, AND OTHER FIELDS…

3. PROVING THAT AI IS THE FUTURE OF
SUPERCOMPUTING…

4. HERE ARE THE “TOP FIVE’ STORIES
HIGHLIGHTING WHAT’S HOT IN HPC AND AI
TOP 5

5. TOP 5
1. Accelerating Quantum Chemistry for Drug Discovery
2. SAP Leonardo Machine Learning Portfolio is First Enterprise Offering to use NVIDIA’s Volta
AI Platform
3. NVIDIA Tesla V100 GPUs Power TYAN Server
4. Baidu Sheds Precision Without Paying Deep Learning Accuracy Cost
5. Achieving Faster AI with NVIDIA GPUs and TensorRT Webinar

6. ACCELERATING QUANTUM CHEMISTRY FOR
DRUG DISCOVERY
In the pharmaceutical industry, drug discovery is a long and
expensive process. It takes an average of 12 years and $2.6
billion to bring a new drug to market. One key to speeding the
drug discovery process is the ability to more accurately
simulate molecular dynamics (MD), to quickly screen millions
of potential drug combinations so researchers can focus their
energy on the most promising options.
All drug discoveries require molecular simulations to
understand their potential efficacy. Molecular energetics,
where millions of molecules are scanned to determine how
they interact with each other, helps in this understanding.
However, to have accurate MD simulations, you need accurate
quantum mechanical (QM) simulations as well. QM simulations
are essential to the process of accurately screening millions of
potential drugs.
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ARTICLE

7. SAP LEONARDO MACHINE LEARNING PORTFOLIO IS
FIRST ENTERPRISE OFFERING TO USE NVIDIA’S VOLTA
AI PLATFORM
2
Earlier this year, SAP and NVIDIA expanded their collaboration
to create business applications based on artificial intelligence.
Now, as NVIDIA’s GPU Technology Conference kicks off in
Munich, Germany, the partnership has gained even further
substance.
SAP installed its first NVIDIA DGX-1 systems – the world’s first
AI supercomputer – in Israel and Potsdam in 2016. This was
followed by the implementation of NVIDIA DGX-1 systems with
NVIDIA Tesla P100 graphics processing units (GPUs) in SAP’s
production data center in St. Leon-Rot, Germany and in SAP’s
Innovation Labs in Palo Alto, California, and Singapore in
September 2017.
ARTICLE

8. NVIDIA TESLA V100 GPUS POWER NEW TYAN SERVER
3
ARTICLE
Today TYAN showcased their latest GPU-optimized platforms
that target the high performance computing and artificial
intelligence sectors at the GPU Technology Conference in
Munich.
“TYAN’s new GPU computing platforms are designed to
provide efficient parallel computing for the analytics of vast
amounts of data. By incorporating NVIDIA’s latest Tesla V100
GPU accelerators, TYAN provides our customers with the
power to accelerate both high performance and cognitive
computing workloads” said Danny Hsu, Vice President of
MiTAC Computing Technology Corporation’s TYAN Business
Unit.

9. BAIDU SHEDS PRECISION WITHOUT PAYING DEEP
LEARNING ACCURACY COST
Today, Baidu Research described another important deep
learning milestone in conjunction with GPU maker,
Nvidia. Teams demonstrated success training networks
with half precision floating point without changing
network hyperparameters and keeping with the same
accuracy derived from single precision. Previous efforts
with mixed lower precision (binary or even 4-bit) have
come with losses in accuracy or major network
modifications, but with some techniques applied to
existing mixed precision approaches, this is no longer the
case. This, of course, means better use of memory and
higher performance—something that can be put to the
test on the newest Nvidia Volta GPUs.
4
ARTICLE

10. ACHIEVING FASTER AI WITH NVIDIA GPUS AND
TENSORRT
5
In this last year, GPU deep learning has gone from a hot
research topic to a large-scale deployment challenge in
major data centers. That’s because deep learning is
extraordinarily effective and now powers every
application, from speech recognition to self-driving cars,
from language translation to better search. Power
efficiency and processing power make GPUs the right fit
for deep learning and inference from the edge to the data
center.
Learn About:
1. How neural nets, frameworks, and GPU architectures
have changed significantly in the last year
2. How to save on cost, while achieving better AI
performance, efficiency, and responsiveness
3. How to unleash the full potential of NVIDIA GPUs with
NVIDIA TensorRT
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