This blog post explains GPU Boost, a new user controllable feature available on Tesla GPUs. Case studies and benchmarks for reverse time migration and an electromagnetic solver are discussed.
The latest Top 500 list of the world’s fastest supercomputers, released November 15th, demonstrates that GPUs are being adopted on a large scale in the HPC space. Three out of the top 5 machines (#1 and #3 in China, and #4 in Japan) feature NVIDIA Tesla GPUs. Also, the list confirms the expected result that the new GPU-based Tianhe-1a machine from China has ousted Jaguar from the top spot.
From a press release:
AUSTIN, Texas, — Financial institutions are turning to graphics processing unit (GPU) computing for real economic and performance benefits. Fast and accurate derivatives pricing model development and accelerated execution speeds are crucial for today’s derivatives marketplace. SciComp Inc. has enhanced SciFinance®, its flagship derivatives pricing software, to help quantitative developers further shorten Monte Carlo derivatives pricing model development time and create models with faster execution speeds. SciFinance® now features support for NVIDIA® Tesla™ 20-series GPUs and CUDA™ 3.0.
“The mathematical problems of pricing derivatives are tailor-made for GPU computing, and Monte Carlo simulations enjoy some of the fastest speed-ups on GPUs: from 50 to over 300 times faster compared to serial code,” said Curt Randall, executive vice president of SciComp. “This execution speed increase makes it feasible to replace grid solutions (CPUs and interconnects) with a GPU system. GPU costs are a tiny percentage of the cost of a grid solution and offer radical reductions in both footprint and power consumption.”
SciFinance takes advantage of new GPU hardware and software from NVIDIA Read the rest of this entry »
From a press release:
New Software Solution Reduces Dependency on CPUs
PORTLAND, Ore.- SC09-Nov. 18, 2009- NVIDIA Corporation (Nasdaq: NVDA) and Mellanox Technologies Ltd. today introduced new software that will increase cluster application performance by as much as 30% by reducing the latency that occurs when communicating over Mellanox InfiniBand to servers equipped with NVIDIA Tesla™ GPUs.
The system architecture of a GPU-CPU server requires the CPU to initiate and manage memory transfers between the GPU and the InfiniBand network. The new software solution will enable Tesla GPUs to transfer data to pinned system memory that a Mellanox InfiniBand solution is able to read and transmit over the network. The result is increased overall system performance and efficiency.
“NVIDIA Tesla GPUs deliver large increases in performance across each node in a cluster, but in our production runs on TSUBAME 1 we have found that network communication becomes a bottleneck when using multiple GPUs,” said Prof. Satoshi Matsuoka from Tokyo Institute of Technology. “Reducing the dependency on the CPU by using InfiniBand will deliver a major boost in performance in high performance GPU clusters, thanks to the work of NVIDIA and Mellanox, and will further enhance the architectural advances we will make in TSUBAME2.0.” Read the rest of this entry »
To assist contestants in the TopCoder and NVIDIA CUDA Superhero Challenge, from August 28th and continuing for one month, RenderStream will offer a promotional discount of up to $500 for the first ten PSC development systems they sell with two or more C1060 cards or $800 for one S1070 integrated with a Twin Dual Quad Server (no more than two systems per customer). In addition, RenderStream will give a free NVIDIA Tesla C1060 to the customer who places highest in the contest.
SAN FRANCISCO, Aug. 11 — Penguin Computing, experts in high performance computing solutions, today announced the immediate availability of “Penguin on Demand” — or POD — a new service that delivers, for the first time, a complete high performance computing (HPC) solution in the cloud. POD extends the concept of cloud computing by making optimized compute resources designed specifically for HPC available on demand. POD is targeted at researchers, scientists and engineers who require surge capacity for time-critical analyses or organizations that need HPC capabilities without the expense and effort required to acquire HPC clusters.
A GPU computing workshop and discussion forum will be held at the UWA University Club Thursday, May 7th. The workshop aims to provide a detailed introduction to GPU computing with CUDA and NVIDIA Tesla computing solutions, and to present research in GPU and Heterogeneous computing being undertaken in Western Australia.
Mark Harris (NVIDIA) will present an introduction to the CUDA architecture, programming model, and the programming environment of C for CUDA, as well as an overview of the Tesla GPU architecture, a live programming demo, and strategies for optimizing CUDA applications for the GPU. To better enable the uptake of this technology, Dragan Dimitrovici from Xenon Systems will provide an overview of CUDA enabled hardware options. The workshop will also include brief presentations of some of the projects using CUDA within Western Australia, including a presentation from Professor Karen Haines (WASP@UWA) on parallel computing strategies required for optimizing applications for GPU and heterogeneous computing.
Please see the workshop flyer for full details.
In this ClusterMonkey article, Andrew Humber, Senior PR Manager for Tesla and CUDA Technologies at NVIDIA Corporation, summarizes the events that made 2008 a truly exciting year for GPU Computing. (A Year in Review from the NVIDIA Tesla Team, ClusterMonkey)
This is a GPGPU event a long time in the making. Since the advent of general-purpose APIs and compilers for GPUs it has been predicted that GPUs would one day be used to help boost the performance of Supercomputers. With the latest release of the Top500 Supercomputer list, that prediction has become a reality.
More details from an NVIDIA press release:
NVIDIA Tesla Powers 29th Most Powerful Supercomputer in the World
SC08—AUSTIN, TX—NOVEMBER 17, 2008—The Tokyo Institute of Technology (Tokyo Tech) today announced a collaboration with NVIDIA to use NVIDIA® Tesla™ GPUs to boost the computational horsepower of its TSUBAME supercomputer. Through the addition of 170 Tesla S1070 1U systems, the TSUBAME supercomputer now delivers nearly 170 TFLOPS of theoretical peak performance, as well as 77.48 TFLOPS of measured Linpack performance, placing it, again, amongst the top ranks in the world’s Top 500 Supercomputers.
“Tokyo Tech is constantly investigating future computing platforms and it had become clear to us that to make the next major leap in performance, TSUBAME had to adopt GPU computing technologies,” said Satoshi Matsuoka, division director of the Global Scientific Information and Computing Center at Tokyo Tech. “In testing our key applications, the Tesla GPUs delivered speed-ups that we had never seen before, sometimes even orders of magnitude – a tremendous competitive boost for our scientists and engineers in reducing their time to solution.”
Speaking to the ease of implementation, Matsuoka continued,
“The entire upgrade was carried out in 1 week, and the TSUBAME supercomputer remained live throughout. This is an unprecedented feat in top-level supercomputing.”
From a press release:
NVIDIA Tesla Makes Personal SuperComputing A Reality
Tesla GPUs Enable Cluster Class Performance On The Desktop at 1/10th The Power
SC08—AUSTIN, TX—NOVEMBER 18 2008— Today, scientific research is carried out on supercomputing clusters, a shared resource that consumes hundreds of kilowatts of power and costs millions of dollars to build and maintain. As a result, researchers must fight for time on these resources, slowing their work and delaying results. NVIDIA and its worldwide partners today announced the availability of the GPU-based Tesla™ Personal Supercomputer, which delivers the equivalent computing power of a cluster, at 1/100th of the price and in a form factor of a standard desktop workstation.