About Calisa Cole

Calisa Cole
Calisa joined NVIDIA in 2003 and currently focuses on marketing related to CUDA, NVIDIA’s parallel computing architecture. Previously she ran Cole Communications, a PR agency for high-tech startups. She majored in Russian Studies at Wellesley and earned an MA in Communication from Stanford. Calisa is married and the mother of three boys. Her favorite non-work activities are fiction writing and playing fast games of online scrabble.
cuda_spotlight

CUDA Spotlight: Women and CUDA

Today we are pleased to launch the new Women and CUDA website.

We received a wide variety of entries from around the world, representing professors, students, researchers, scientists and domain experts. We had recognized several participants earlier as CUDA Spotlights, including Valerie Halyo of Princeton and Monica Syal of Advanced Rotorcraft Technology.

CUDA-1

CUDA Fellow Lorena Barba comments: “This is a good way to remind people that women write code, participate in open-source projects, and invent things. It’s important to make the technology world more attractive to female students.” Dr. Barba is an associate professor of Engineering and Applied Science at George Washington University.

Congrats to the women on our inaugural Women and CUDA list:

Sonia Lopez Alarcon, Rochester Institute of Technology | Heterogeneous Computing
Rommie Amaro, University of California, San Diego | Biological Systems
Michela Becchi, University of Missouri | GPU Virtualization Continue reading

cuda_spotlight

CUDA Spotlight: GPU-Accelerated Nanotechnology

bathe-mark

Our Spotlight is on Dr. Mark Bathe, Associate Professor of Biological Engineering at the Massachusetts Institute of Technology.

Mark’s lab focuses on in silico design and programming of synthetic nucleic acid scaffolds for engineering light-harvesting antennas, multi-enzyme cascades, cellular delivery vehicles, and fluorescent biomolecular probes, which he assays using innovative quantitative imaging techniques.

The following is an excerpt from our interview (read the complete Spotlight here).

_____________________________________

NVIDIA: Mark, tell us about your work with structural nucleic acids and DNA nanotechnology.
Mark: DNA is best known to us as the molecule of life: It stores our genetic information and transmits that information from generation to generation.

A lesser known, powerful alternative use for DNA is that of a programmable structural element for engineering molecular scaffolds of precise shape and size at the nanometer-scale.

This molecular engineering paradigm dates back to early work by Nadrian Seeman in the 1980s, when he demonstrated theoretically that DNA could be programmed to form large-scale synthetic assemblies due to its unique and highly specific basepairing properties.

nanometer-diameterSince that landmark work, the field of molecular engineering using nucleic acids has witnessed explosive growth. Unlike proteins, DNA is highly programmable structurally because it can be designed to robustly self-assemble into large-scale molecular architectures of precise nanometer-scale structural features, dimensions, and mechanical properties.

These assemblies can subsequently be functionalized chemically using lipids, dyes, and proteins for diverse applications in biomolecular science and technology.

The rapidly decreasing cost of synthetic DNA, together with rational computational design rules, now enable a plethora of structured nanoscale materials to be designed, with the ultimate aim of replicating the function of biological protein assemblies that have evolved over billions of years.
Continue reading

cuda_spotlight

CUDA Spotlight: GPU-Accelerated Deep Learning

Ren-Wu-BaiduOur Spotlight is on Dr. Ren Wu, a distinguished scientist at Baidu’s Institute of Deep Learning (IDL).

He is known for his pioneering research in using GPUs to accelerate big data analytics and his contribution to large-scale clustering algorithms via the GPU. Ren was a speaker at GTC14 and was originally featured as a CUDA Spotlight in 2011 when he worked at HP Labs.

[Editor's note: On May 16, Baidu announced the hiring of Dr. Andrew Ng to lead Baidu's Silicon Valley Research Lab.]

The following is an excerpt from our interview (read the complete Spotlight here).
______________________________________________________________________________

NVIDIA: Ren, why is GPU computing important to your work?
Ren: A key factor in the progress we are making with deep learning is that we now have much greater computing resources in our hands.

Today one or two workstations with a few GPUs has the same computing power as the fastest supercomputer in the world 15 years ago, thanks to GPU computing and NVIDIA’s vision.
Continue reading

cuda_spotlight

CUDA Spotlight: GPU-Accelerated Deep Neural Networks

dan-ciresan-idsiaThis week’s Spotlight is on Dr. Dan Ciresan, a senior researcher at IDSIA in Switzerland and a pioneer in using CUDA for Deep Neural Networks (DNNs).

His methods have won international competitions on topics such as classifying traffic signs and recognizing handwritten Chinese characters. Dan presented a session on Deep Neural Networks for Visual Pattern Recognition at GTC in March 2014.

The following is an excerpt from our interview (read the complete Spotlight here).

NVIDIA: Dan, tell us about your research at IDSIA.
Dan: I am continuously developing my Deep Neural Network framework and looking for new interesting applications. In the last three years we have won five international competitions on pattern recognition and improved the state of the art by 20-40% on many well-known datasets. One of our current projects involves developing an automatic system for trail following. When ready, we plan to mount it on a quadcopter and let it navigate through the woods.

Continue reading

cuda_spotlight

CUDA Spotlight: GPU-Accelerated Agent-Based Simulation of Complex Systems

Paul-RichmondThis week’s Spotlight is on Dr. Paul Richmond, a Vice Chancellor’s Research Fellow at the University of Sheffield (a CUDA Research Center). Paul’s research interests relate to the simulation of complex systems and to parallel computer hardware.

The following is an excerpt from our interview (read the complete Spotlight here).

NVIDIA: Paul, tell us about FLAME GPU.
Paul: Agent-Based Simulation is a powerful technique used to assess and predict group behavior from a number of simple interacting rules between communicating autonomous individuals (agents). Individuals typically represent some biological entity such as a molecule, cell or organism and can therefore be used to simulate systems at varying biological scales.

The Flexible Large-scale Agent Modelling Environment for the GPU (FLAME GPU) is a piece of software which enables high level descriptions communicating agents to be automatically translated to GPU hardware. With FLAME GPU, simulation performance is enormously increased over traditional agent-based modeling platforms and interactive visualization can easily be achieved. The GPU architecture and the underlying software algorithms are abstracted from users of the FLAME GPU software, ensuring accessibility to users in a wide range of domains and application areas.

NVIDIA: How does FLAME GPU leverage GPU computing?
Paul: Unlike other agent-based simulation frameworks, FLAME GPU is designed from the ground up with parallelism in mind. As such it is possible to ensure that agents and behavior are mapped to the GPU efficiently in a way which minimizes data transfer during simulation. Continue reading

cuda_spotlight

CUDA Spotlight: GPU-Accelerated Speech Recognition

Ian-Lane-CMUThis week’s Spotlight is on Dr. Ian Lane of Carnegie Mellon University. Ian is an Assistant Research Professor and leads a speech and language processing research group based in Silicon Valley. He co-directs the CUDA Center of Excellence at CMU with Dr. Kayvon Fatahalian.

The following is an excerpt from our interview (read the complete Spotlight here).

NVIDIA: Ian, what is Speech Recognition?
Ian: Speech Recognition refers to the technology that converts an audio signal into the sequence of words that the user spoke. By analyzing the frequencies within a snippet of audio, we can determine what sounds within spoken language a snippet most closely matches, and by observing sequences of these snippets we can determine what words or phrases the user most likely uttered.

Speech Recognition spans many research fields, including signal processing, computational linguistics, machine learning and core problems in computer science, such as efficient algorithms for large-scale graph traversal. Speech Recognition also is one of the core technologies required to realize natural Human Computer Interaction (HCI). It is becoming a prevalent technology in interactive systems being developed today.

NVIDIA: What are examples of real-world applications?
Ian: In recent years, speech-based interfaces have become much more prevalent, including applications such as virtual personal assistants, which include systems such as Siri from Apple or Google Voice Search, as well as speech interfaces for smart TVs and in-vehicle systems. Continue reading

cuda_spotlight

CUDA Spotlight: GPU-Accelerated Quantum Chemistry

todd-martinez-stanford

This week’s Spotlight is on Professor Todd Martínez of Stanford.

Professor Martínez’ research lies in the area of theoretical chemistry, emphasizing the development and application of new methods which accurately and efficiently capture quantum mechanical effects.

Professor Martínez pioneered the use of GPU technology for computational chemistry, culminating in the TeraChem software package that uses GPUs for first principles molecular dynamics. He is a founder of PetaChem, the company that distributes this software.

The following is an excerpt from our interview (you can read the complete Spotlight here).

NVIDIA: Todd, tell us about TeraChem.
Todd: TeraChem simulates the dynamics and motion of molecules, solving the electronic Schrodinger equation to determine the forces between atoms. This is often called first principles molecular dynamics or ab initio molecular dynamics.

The primary advantage over empirical force fields (for example, often used for protein structure) is that chemical bond rearrangements and electron transfer can be described seamlessly. Continue reading

cuda_spotlight

If the Virtual Zapato Fits, Wear It! (GPU-Accelerated Augmented Reality)

Foto_NestorThis week’s Spotlight is on Néstor Gómez, CEO of Artefacto Estudio in Mexico City.

Artefacto Estudio is a developer of interactive applications and games. The company’s projects include a real-time virtual shoe fitting kiosk that allows people to “try on” shoes using augmented reality powered by Microsoft Kinect and GPU computing (see the video).

The following is an excerpt from our interview (you can read the complete Spotlight here).

NVIDIA: Néstor, tell us a bit about Artefacto Estudio.
Néstor: Artefacto is an independent development studio. We integrate solutions using cutting-edge technologies like Microsoft Kinect, Oculus Rift and Leap Motion.

NVIDIA: How did you become involved in the shoe industry?
Néstor: An ad agency, Kempertrautmann, was seeking a technology partner to work on a prototype for a virtual shoe fitting exhibit for Goertz, the German shoe company.

NVIDIA: Tell us about the prototype you created for Goertz. Continue reading

cuda_spotlight

CUDA Spotlight: GPU-Accelerated Cosmology

DBard-video-photoThis week’s Spotlight is on Dr. Debbie Bard, a cosmologist at the Kavli Institute for Particle Astrophysics and Cosmology (KIPAC).

KIPAC members work in the Physics and Applied Physics Departments at Stanford University and at the SLAC National Accelerator Laboratory.

To handle the massive amounts of data involved in cosmological measurements, Debbie and her colleagues Matt Bellis (now an assistant professor at Siena College) and Mark Allen (now a data scientist at Chegg) teamed up to explore the potential of GPU computing and CUDA.

They concluded that “GPUs are a useful tool for cosmological calculations, allowing calculations to be made one or two orders of magnitude faster.” Their results were presented in a paper titled Cosmological Calculations on the GPU, which appeared earlier this year in Astronomy and Computing.

The following is an excerpt from our interview (you can read the complete Spotlight here). Continue reading

cuda_spotlight

CUDA Spotlight: GPU-Accelerated Cancer Detection

Diego Rivera HeadshotThis week’s Spotlight is on Diego Rivera, a senior software engineer at Hologic, Inc. Hologic is a leading developer of medical imaging systems and surgical products, with an emphasis on serving the healthcare needs of women throughout the world.

The following is an excerpt from our interview (you can read the complete Spotlight here).

NVIDIA: Diego, tell us about your role at Hologic.
Diego: I’m part of a team that has been able to deliver great solutions for breast cancer detection. I’m a lead engineer in design and implementation of GPU-accelerated high-performance applications in the areas of tomosynthesis (3D-mammography), digital mammography, computer-aided design, and specimen radiography systems.

NVIDIA: What are some key challenges in your field?
Diego: With all the advantages of the digital era, film-based mammography has not yet vanished. Introducing a new modality such as tomosynthesis is challenging because the new capabilities have also led to new image management requirements (in areas such as storage, image reviewing and vendor viewing interoperability).

Despite the challenges, the tomosynthesis modality has proved more advantageous in early detection of cancer cases than the usage of standard digital images and has the potential of reducing the number of false positives. Our goal is to enable everyone to embrace this medium.

Hologic's Selenia Dimensions 3D Breast Tomosynthesis System
Hologic’s Selenia Dimensions 3D Breast Tomosynthesis System

NVIDIA: What role does GPU computing play in your work?
Diego: It has allowed us to process and reprocess images in real time. The impact of this is that there is no wait time added for screening and diagnostic results, which in turn minimizes the patient’s anxiety.

One of our objectives is to improve the patient experience by controlling dose and time in compression without sacrificing image quality. Real-time tomosynthesis would simply not be possible without GPUs. Our solution is deployed in a variety of hospitals and health care centers, including Massachusetts General Hospital and Bethesda Women’s Health Center. Continue reading