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GTC ON-DEMAND

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Abstract:

There are 14 million new cancer cases and 8.2 million cancer-related deaths worldwide per year.Innovation in the fight against cancer requires a multi-faceted approach. As patients and as stakeholders, healthcare ecosystem experts in genomics, proteomics, imaging, medicine and data sciences are cooperating in new ways. GPU computing, integrated data and novel algorithms enable the use of deep learning and artificial intelligence to transform cancer research and care. Dr. Jerry S.H. Lee, Whitehouse Cancer Moonshot Deputy Director for Research and Technology, will facilitate a thought provoking panel discussion on leveraging Artificial Intelligence to fight cancer.

There are 14 million new cancer cases and 8.2 million cancer-related deaths worldwide per year.Innovation in the fight against cancer requires a multi-faceted approach. As patients and as stakeholders, healthcare ecosystem experts in genomics, proteomics, imaging, medicine and data sciences are cooperating in new ways. GPU computing, integrated data and novel algorithms enable the use of deep learning and artificial intelligence to transform cancer research and care. Dr. Jerry S.H. Lee, Whitehouse Cancer Moonshot Deputy Director for Research and Technology, will facilitate a thought provoking panel discussion on leveraging Artificial Intelligence to fight cancer.

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Topics:
Healthcare and Life Sciences
Type:
Panel
Event:
GTC Washington D.C.
Year:
2016
Session ID:
DCS16179
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Abstract:

Join this session to learn how to use GPUs and CUDA programming to achieve order-of-magnitude speedup even for large codes that are more complex than tutorial examples. We'll cover our multi-year effort on heterogeneous CPU-GPU accelerating for the GROMACS package for molecular dynamics simulations on a wide range of architectures. We'll introduce new results where CUDA has made it possible to accelerate the costly 3D image reconstruction used in single-particle cryo-electron microscopy (cryo-EM) by 20-200X. You'll learn how you can use these tools in your application work, and what strategies to pursue to accelerate difficult codes where neither libraries nor directives use useful, and even moving computational kernels to CUDA seems to fail.

Join this session to learn how to use GPUs and CUDA programming to achieve order-of-magnitude speedup even for large codes that are more complex than tutorial examples. We'll cover our multi-year effort on heterogeneous CPU-GPU accelerating for the GROMACS package for molecular dynamics simulations on a wide range of architectures. We'll introduce new results where CUDA has made it possible to accelerate the costly 3D image reconstruction used in single-particle cryo-electron microscopy (cryo-EM) by 20-200X. You'll learn how you can use these tools in your application work, and what strategies to pursue to accelerate difficult codes where neither libraries nor directives use useful, and even moving computational kernels to CUDA seems to fail.

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Topics:
Healthcare and Life Sciences, HPC and AI
Type:
Talk
Event:
GTC Washington D.C.
Year:
2016
Session ID:
DCS16141
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Abstract:
This session will showcase how the latest CUDA devices have expanded beyond x86 in high performance computing (HPC), and are enabling new combinations with power-efficient ARM or extreme-performance Power processors. In particular we will describe the challenges in accelerating our molecular simulation code GROMACS, combined with general HPC conclusions. We will cover challenges and advantages compared to x86 and discuss strategies for scheduling and partitioning work over wide ranges of GPU & CPU hardware, in particular for heterogeneous acceleration, large-scale parallelization, and achieving outstanding scientific code performance. The registrants should ideally have some experience from scientific computing and/or biomolecular simulation.
This session will showcase how the latest CUDA devices have expanded beyond x86 in high performance computing (HPC), and are enabling new combinations with power-efficient ARM or extreme-performance Power processors. In particular we will describe the challenges in accelerating our molecular simulation code GROMACS, combined with general HPC conclusions. We will cover challenges and advantages compared to x86 and discuss strategies for scheduling and partitioning work over wide ranges of GPU & CPU hardware, in particular for heterogeneous acceleration, large-scale parallelization, and achieving outstanding scientific code performance. The registrants should ideally have some experience from scientific computing and/or biomolecular simulation.  Back
 
Topics:
HPC and Supercomputing, Life & Material Science
Type:
Talk
Event:
GTC Silicon Valley
Year:
2015
Session ID:
S5434
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Abstract:

Learn how to perform molecular dynamics simulations reaching microsecond-per-day performance on GPUs, how to achieve impressive GPU acceleration of a code that was already extremely hand-tuned for x86 CPUs, and how we hope to take it even further in the future. GROMACS is one of the most widespread programs in the world to simulate biomolecular dynamics, and has long been accelerated for CPUs with handtuned assembly code. This session will cover our challenges and successes in achieving significantly higher absolute performance with CUDA in GROMACS compared to extremely tuned CPU code both on low-end systems and massively parallel supercomputers. Join us to learn about the overall architectural decisions and features of this heterogeneous multi-level parallelization, see examples of application performance, and participate in a discussion about how future molecular simulation needs to focus on efficient throughput and sampling to achieve scaling.

Learn how to perform molecular dynamics simulations reaching microsecond-per-day performance on GPUs, how to achieve impressive GPU acceleration of a code that was already extremely hand-tuned for x86 CPUs, and how we hope to take it even further in the future. GROMACS is one of the most widespread programs in the world to simulate biomolecular dynamics, and has long been accelerated for CPUs with handtuned assembly code. This session will cover our challenges and successes in achieving significantly higher absolute performance with CUDA in GROMACS compared to extremely tuned CPU code both on low-end systems and massively parallel supercomputers. Join us to learn about the overall architectural decisions and features of this heterogeneous multi-level parallelization, see examples of application performance, and participate in a discussion about how future molecular simulation needs to focus on efficient throughput and sampling to achieve scaling.

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Topics:
Quantum Chemistry, HPC and Supercomputing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2013
Session ID:
S3283
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Abstract:

Learn about the first multi-node, multi-GPU-enabled release 4.6 of GROMACS from Dr. Erik Lindahl, the project leader for this popular molecular dynamics package. GROMACS 4.6 allows you to run your models up to 3X faster compare to the latest state-of-the-art parallel AVX-accelerated CPU-code in GROMACS. Dr. Lindahl will talk about the new features of the latest GROMACS 4.6 release as well as future plans. You will learn how to download the latest accelerated version of GROMACS and which features are GPU supported. Dr. Lindahl will cover GROMACS performance on the very latest NVIDIA Kepler hardware and explain how to run GPU-accelerated MD simulations. You will also be invited to try GROMACS on K20 with a free test drive and experience all the new features and enhanced performance for yourself: http://www.nvidia.com/gputestdrive 

Learn about the first multi-node, multi-GPU-enabled release 4.6 of GROMACS from Dr. Erik Lindahl, the project leader for this popular molecular dynamics package. GROMACS 4.6 allows you to run your models up to 3X faster compare to the latest state-of-the-art parallel AVX-accelerated CPU-code in GROMACS. Dr. Lindahl will talk about the new features of the latest GROMACS 4.6 release as well as future plans. You will learn how to download the latest accelerated version of GROMACS and which features are GPU supported. Dr. Lindahl will cover GROMACS performance on the very latest NVIDIA Kepler hardware and explain how to run GPU-accelerated MD simulations. You will also be invited to try GROMACS on K20 with a free test drive and experience all the new features and enhanced performance for yourself: http://www.nvidia.com/gputestdrive 

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Topics:
Molecular Dynamics
Type:
Webinar
Event:
GTC Webinars
Year:
2013
Session ID:
GTCE030
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Speakers:
Erik Lindahl
 
Topics:
Molecular Dynamics
Type:
Talk
Event:
Supercomputing
Year:
2011
Session ID:
SC106
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