By including "credible data" extracted from the Twitter social networking service, the study of earthquakes and tsunamis is being systematically transformed into a big data analytics problem. The challenge of establishing geophysically credible tweets is considered through a combination of deep learning and semantics (that is, knowledge representation). More specifically, tweet classification via GPU-based platforms for deep learning is compared and contrasted with previous work based on the use of in-memory computing via Apache Spark. Although there remains cause for optimism in augmenting traditional scientific data with that derived from social networking, ongoing research is aimed at providing utility in practice. The motivation for success remains strong, as establishing a causal relationship between earthquakes and tsunamis remains problematical, and this in turn complicates any ability to deliver timely, accurate messaging that could prove life-critical. Finally, we'll consider the applicability of this approach to other disaster scenarios (for example, the Deepwater Horizon oil spill).
CUDA-ready clusters enable developers to: Focus on coding, not maintaining infrastructure (drivers, configs) and toolchains (compilers, libraries) Routinely keep pace with innovation - from the latest in GPU hardware to the CUDA toolkit itself Cross-develop with confidence and ease - maintain, and shift between, highly customized CUDA development environments Exercise their preference in programming GPUs - choose CUDA or OpenCL or OpenACC and combine appropriately (with, for example, the Message Passing Interface, MPI) Exploit the convergence of HPC and Big Data Analytics - make simultaneous use HPC and Hadoop services in GPU applications Make use of private and public clouds - create a CUDA-ready cluster in a cloud or extend an on-site CUDA infrastructure into a cloud In this webinar, participants will learn how Bright Cluster Manager provisions, monitors and manages CUDA-ready clusters for developer advantage. Case studies will be used to illustrate all six advantages for Bright developers. Specific attention will be given to: Cross-developing under CUDA 6.0 and CUDA 6.5 with Kepler-architecture GPUs (e.g., the NVIDIA Tesla K80 GPU accelerator) The challenges and opportunities for making use of private (using OpenStack) and public (using Amazon Web Services) clouds in GPU applications
Recent advances in software development and compilers to exploit the power of GPUs are leading to increased interest in the OpenACC programming model. Development tools are the key to success - and Allinea DDT is leading the charge with efficient and easy to use debugging for this model. This talk will outline the latest advances in debugging support for CUDA - including OpenACC, Dynamic Parallelism and nested kernels, and Kepler 2 - and show how users are using this support to solve challenging software problems.
Bright Cluster Manager delivers a comprehensive and integrated CUDA-ready solution for those who seek to make optimal use of their GPU-based environments for HPC. Bright provisions, monitors and manages systems with NVIDIA GPUs within cluster-management hierarchies.
Join Ian Lumb, Bright Evangelist and learn how Bright:
From advanced algorithms to large-scale datacenter implementations, GPUs are literally rewriting the rules for energy exploration and data processing. GPU-based solutions for HPC and visualization deliver exceptional-quality results in a timely and cost-effective manner that minimizes Total Cost of Ownership. By drawing on their collective experience in energy exploration and data processing, industry veterans from NVIDIA, R-Associates Inc., and Bright Computing will share:HPC trends in energy exploration and data processing - GPU-accelerated seismic processing clusters deliver 4-6X more throughput, and make high-resolution subsurface images affordable using advanced algorithms that improve drilling decisionsTrends in energy-exploration visualization - virtualized GPUs improve economies of scale by `pushing pixels’ to the `thin devices’ of globally distributed exploration teams, keeping voluminous datasets secure within datacentersPioneering innovations in non-blocking systems architecture that permit ultra-dense GPU configurations - 20 GPUs in a 7U chassis!Best practices for putting GPUs to productive use - automated installation of CUDA drivers, tools, and toolkits, GPU-specific metrics for monitoring and rules-based actions, plus health checks based on nvidia-healthmon