Share:   Facebook Twitter Linked-in Google Reddit Stumbleupon email
CUDA Week in Review Newsletter
Fri., July 25, 2014, Issue #113 Newsletter Home
Welcome to CUDA: Week In Review
News and resources for the worldwide GPU and parallel programming community.


Did you know that CUDA 6.5 includes a new suite of API functions to simplify calculating occupancy and choosing a high-occupancy thread block size? Learn more on the Parallel Forall blog.


Women and CUDA Women and CUDA
Following the Women@GTC event in March, NVIDIA invited women developers, researchers and scientists to share how they utilize GPU computing in their work.

Today we launched the new Women and CUDA website, representing students, professors and industry experts from around the world. Take a look.


GTC Japan
Last week, 1500+ people converged at the GPU Technology Conference in Tokyo, hosted by NVIDIA and the Tokyo Institute of Technology. NVIDIA Fellow David Kirk kicked off the conference with a keynote outlining the broad reach of GPUs and citing several examples of how CUDA is used for cutting-edge research in Japan:
  • Tokyo Tech is using GPUs to accelerate genomics, identifying frequent k-mers in DNA sequences.
  • At Waseda University, researchers are simulating the adaptive behavior selection of humanoid robots using deep neural networks on GPUs.
  • At Hokkaido University, real-time deformation of soft biological tissues is being simulated to provide more realism for surgical training. (Read blog post here)
New Version of PGI 2014 Now Available
The Portland Group (PGI) has released version 14.7 of its PGI Accelerator compliers, adding support for CUDA Managed Data from within CUDA Fortran, along with a number of other new features and enhancements. Learn more.


back to the top
Aug. 6: Deep Neural Networks for Visual Pattern Recognition, D. Ciresan, IDSIA
Aug. 12: Asynchronous Operations & Dynamic Parallelism in CUDA, D. Cyca, Acceleware
Aug. 20: Ten Billion Parameter Neural Networks in Your Basement, A. Coates, Baidu
Sept. 25: HOOMD-blue 1.0: Molecular Dynamics on GPUs, J. Anderson, J. Glaser, University of Michigan


back to the top
Subscribe to the Parallel Forall RSS feed Parallel Forall:
CUDA Pro Tip: Occupancy API Simplifies Launch Configuration, M. Harris
Low-Power Sensing and Autonomy with NVIDIA Jetson TK1, D. Franklin, GE Intelligent Platforms
CUDA Pro Tip: Profiling MPI Applications, J. Kraus

Supercomputing Green500 List Filling Out with GPUs, S. Gupta
GPU System Wins Student Supercomputing Competition at ISC '14, G. Millington


back to the top


  Aug. 10-14, 2014, Vancouver, Canada

Euro-Par 2014
  Aug. 25-29, 2014, Porto, Portugal

CUDA Course (Acceleware)
  Sept. 2-5, 2014, Houston, Texas
(Oil and Gas focus)

GPUs in Databases (ADBIS Workshop)
  Sept. 7, 2014, Ohrid, Republic of Macedonia

GPU Computing for Oil & Gas (EAGE Workshop)
  Sept. 7-10, 2014, Crete, Greece

ICPP-2014 (Conference on Parallel Processing)
  Sept. 9-12, 2014, Minneapolis, MN

GPU Computing in Physics and Astrophysics
  Sept. 15-17, 2014, Rome, Italy
Presenters include Massimiliano Fatica, NVIDIA

Deep Learning Unbounded: Intelligence as a Service
  Sept. 16, 2014, Stanford, Calif.
Sponsored by VLAB

IEEE Cluster 2014
  Sept. 22-26, 2014, Madrid, Spain

CUDA Course (Acceleware)
  Sept. 23-26, 2014, Frankfurt, Germany


  Nov. 16-21, 2014, New Orleans, LA

CUDA Course (Acceleware)
  Dec. 2-5, 2014, New York
(Finance focus)

(To list an event, email:


back to the top

Online Learning

Udacity | Coursera | APC Russia

GPU-Accelerated Libraries

Adding GPU acceleration to an application can be as easy as calling a library function. Check out these high-performance libraries from NVIDIA and partners.

CUDA Consulting

Training, programming and project development services are available from CUDA consultants around the world. To be considered for inclusion on list, email:

GPU Test Drive

Want to try Tesla K40 for free? Sign up here.


Check out our new series of short videos about CUDA.

Tell Us Your CUDA Story

If you are a CUDA developer, tell us how you are using CUDA.

GPU-Accelerated Apps

See updated list of 270+ GPU-accelerated applications.

GPU Meetups

Learn about Meetups in your city, or start one up.

CUDA Documentation

The CUDA documentation site includes release notes, programming guides, manuals and code samples.

NVIDIA Developer Forums

Join us on the NVIDIA DevTalk forums to share your experience and learn from other developers. You can also ask questions on Stack Overflow, using the ’cuda’ tag.

GPU Computing on Twitter

For daily updates about GPU computing and parallel programming, follow @gpucomputing on Twitter.



CUDA on the Web

CUDA Spotlights
CUDA Newsletters
GPU Test Drive

NVIDIA Newsletters

Sign up for NVIDIA Newsletters, including Media & Entertainment, GeForce, NVIDIA GRID VDI and Shield.

Please fill out our reader survey so we can improve in 2014. Thanks!


CUDA® is a parallel computing platform and programming model invented by NVIDIA. It enables dramatic increases in computing performance by harnessing the power of the graphics processing unit (GPU). NVIDIA provides a complete toolkit for programming on the CUDA architecture, supporting standard computing languages such as C, C++ and Fortran. Send comments to
Copyright © 2014 NVIDIA Corporation. All rights reserved. 2701 San Tomas Expressway, Santa Clara, CA 95050.