Nvidia reveals GPU for high performance computing
Nvidia has launched a new graphics processing unit (GPU) which it says will create the world's most powerful computer.
Thousands of the chipmaker's new Tesla K20 graphics card will be incorporated into the new Titan supercomputer at the Oak Ridge National Laboratory in Tennessee and the Blue Waters system at the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign.
“In the two years since Fermi was launched, hybrid computing has become a widely adopted way to achieve higher performance for a number of critical HPC applications,” said Earl C. Joseph, program vice president of High-Performance Computing at IDC.
“Over the next two years, we expect that GPUs will be increasingly used to provide higher performance on many applications.”
The new Nvidia Tesla K10 and K20 GPUs, based on its Kepler GPU computing architecture, are computing accelerators built to handle the most complex high performance computing (HPC) scientific and technical applications in the world.
Designed with an intense focus on high performance and extreme power efficiency, Kepler is three times as efficient as its predecessor, the Nvidia Fermi architecture, which itself established a new standard for parallel computing when introduced two years ago.
“Kepler will establish GPUs broadly into technical computing, due to their ease of use, broad applicability and efficiency,” said Bill Dally, chief scientist and senior vice president of research at Nvidia.
The Tesla K10 and K20 GPUs were introduced at the GPU Technology Conference (GTC) in San Jose, California, this week.
Nvidia says it has developed a set of innovative architectural technologies that make the Kepler GPUs high performing and highly energy efficient, as well as more applicable to a wider set of developers and applications.
- SMX Streaming Multiprocessor: the basic building block of every GPU, the SMX streaming multiprocessor was redesigned from the ground up for high performance and energy efficiency.
Nvidia says it delivers up to three times more performance per watt than the Fermi streaming multiprocessor, making it possible to build a supercomputer that delivers one petaflop of computing performance in just 10 server racks.
SMX’s energy efficiency was achieved by increasing its number of CUDA architecture cores by four times, while reducing the clock speed of each core, power-gating parts of the GPU when idle and maximizing the GPU area devoted to parallel-processing cores instead of control logic.
- Dynamic Parallelism: enables GPU threads to dynamically spawn new threads, allowing the GPU to adapt dynamically to the data.
It greatly simplifies parallel programming, enabling GPU acceleration of a broader set of popular algorithms, such as adaptive mesh refinement, fast multipole methods and multigrid methods.
- Hyper-Q: enables multiple CPU cores to simultaneously use the CUDA architecture cores on a single Kepler GPU.
This dramatically increases GPU utilization, slashing CPU idle times and advancing programmability and is ideal for cluster applications that use MPI.
“We designed Kepler with an eye towards three things: performance, efficiency and accessibility,” said Jonah Alben, senior vice president of GPU Engineering and principal architect of Kepler at NVIDIA.
“It represents an important milestone in GPU-accelerated computing and should foster the next wave of breakthroughs in computational research.”
See more on NVIDIA Tesla GPUs
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