GPUs Help Unravel “Origami-like” Behavior of Human Genome

By George Millington on January 15, 2015

Albert Einstein. Martha Stewart. Dale Earnhardt, Jr. The guy who helps you with your taxes.

The information needed for the development and functioning of each of them – and of all living things – is in our genomes.

This genetic “instruction book” contains about 3 billion base pairs of DNA. That includes all the information needed for our bodies to function, grow, fight off diseases and do millions of other things. But while the genome in every cell of the body is identical, each type of cell needs to be different to serve its specific purpose.

So how do the genes within, say, skin cells, or lung cells or muscle cells, turn on the functions they need? How do they turn off the ones they don’t? Researchers aided by NVIDIA Tesla GPUs – part of the Tesla accelerated computing platform of GPU accelerators and enabling software – just unraveled a key piece of this mystery.

NVIDIA Brings Maxwell to Millions of Gamers with GeForce GTX 960

By Justin Walker on January 22, 2015

Today’s introduction of GeForce GTX 960 brings Maxwell’s powerful and efficient architecture to more gamers than ever before. The GTX 960 offers the same incredible Maxwell performance and advanced technologies of its big brother the GTX 980, but at a more affordable $199 price point. That means more and more gamers can enjoy next-generation PC gameplay, continuing to fuel the PC gaming revolution.

GPU Pro Tip : CUDA 7 Streams Simplify Concurrency

Heterogeneous computing is about efficiently using all processors in the system, including CPUs and GPUs. To do this, applications must execute functions concurrently on multiple processors. CUDA Applications manage concurrency by executing asynchronous commands in streams, sequences of commands that execute in order. Different streams may execute their commands concurrently or out of order with respect to each other.

The Power of C++11 in CUDA 7
____________
[CSF] Thomas H.V. Dupont
Founder of the team CRUNCHERS SANS FRONTIERES 2.0
www.crunchersansfrontieres

PDF files are included for some past GTC presentations. 100's of presentations planned for NVidia GTC.

https://registration.gputechconf.com/form/session-listing&doSearch=true&additional_parameter_selector=none&queryInput=&topic_selector=Developer+-+Algorithms&type_selector=none

https://registration.gputechconf.com/form/session-listing&doSearch=true&additional_parameter_selector=none&queryInput=&topic_selector=Life+%26+Material+Science&type_selector=none

S5579 - GPU DNA Sequencing Base Quality Recalibration
Mauricio Carneiro Group Lead, Computational Technology Development, Broad Institute of MIT and Harvard Mauricio Carneiro Nuno Subtil Senior System Software Engineer, CUDA Libraries & Algorithms, NVIDIA Nuno Subtil

Base recalibration is a crucial step in data processing for DNA and RNA sequencing. Established in 2010 by our group in conjunction with the 1000 Genomes project, recalibrating the probability of error for each base in a genome based on counting observations and re-modeling the empirical error has proven to correctly down estimate the systematic errors made by the sequencing instrument allowing bayesian variant calling algorithms to make the most accurate choice. The task of counting observations in the entire genome is daunting and slow. In this talk we will show how we adapted the algorithm for GPU processing to improve the very long runtimes of this process and how the use of GPUs puts us one step closer to enable fast diagnostics of critical patients in need of a fast answer.

S5434 - High-Performance Molecular Simulation With GROMACS: Heterogeneous Acceleration on x86, ARM & Power
Erik Lindahl Professor, KTH Royal Institute of Technology

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.

S5480 - GPU-Optimized Algorithms for Coarse-Grained MD Simulations of Protein-Nanoparticle Biocorona Formation
Samuel Cho Assistant Professor, Wake Forest University
Samuel Cho

We will describe the GPU-optimized algorithms we developed in order to perform novel coarse-grained MD simulations of 15 apolipoproteins (243 residues each) interacting with a silver nanoparticle, represented by 500 individual beads. The advancement of nanomedicine that can deliver drugs into areas of the cells that were previously inaccessible are becoming realized through nanoparticle development, but they readily interact with biomolecular species that result in biocorona formation that result in nanotoxicity. We will outline the GPU-optimized neighbor list and cell list algorithms, as well as bit-wise shift compression algorithms that decreases the data transfer between GPUs, that were necessary to perform these MD simulations.