NVIDIA Tesla GPUs To Communicate Faster Over Mellanox InfiniBand Networks
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."
"In GPU-based clusters, most of the compute intensive processing is running on the GPUs," said Gilad Shainer, director of high performance computing and technical marketing at Mellanox Technologies. "It's a natural evolution of the system architecture to enable GPUs to communicate more intelligently over InfiniBand. This helps create a computing platform that will enable future Exascale computing and dramatically increase performance for a broad spectrum of applications."
"Anyone who cares about performance in their datacenter uses InfiniBand," said Andy Keane, general manager, Tesla business at NVIDIA. "This new feature will further improve application performance on GPU-based clusters by reducing the dependency on the CPU for communicating over InfiniBand."
This software capability will be available in the NVIDIA CUDA architecture toolkit beginning in Q2 2010 and will work on existing Tesla S1070 1U computing systems and Tesla M1060 module-based clusters and also with the new Tesla 20-series S2050 and S2070 1U systems.
NVIDIA awakened the world to the power of computer graphics when it invented the graphics processing unit (GPU) in 1999. Since then, it has consistently set new standards in visual computing with breathtaking, interactive graphics available on devices ranging from portable media players to notebooks to workstations. NVIDIA's expertise in programmable GPUs has led to breakthroughs in parallel processing which make supercomputing inexpensive and widely accessible. Fortune magazine has ranked NVIDIA #1 in innovation in the semiconductor industry for two years in a row. For more information, see http://www.nvidia.com.
Latest News Posts
- NVIDIA's purported Volta-based TITAN Xv spotted
- Joss Wheddon taking over 'Justice League' directing duty
- Red Dead Redemption 2 delay causes stock dive
- Destiny 2 on PC: no dedicated servers
- Red Dead Redemption 2 delayed to Spring 2018
- ASUS RT-AC1900p Wireless Router Review
- ASUS RT-AC1900p Wireless Router Review
- GA-Z97X-UD5H-BK with SSD M.2 Plextor M8PeG 256gb. low speed after shut down
- Prey Review: Dark Stars, Darker Thoughts
- Alien Covenant Movie Review
- Western Digital sets new standard with latest generation in popular HGST-brand Ultrastar SAS SSD family
- AMD raises expectations for server performance, unveils EPYC processor brand for the datacenter
- HighPoint's SSD7101 PCIe board-sized drive series integrate Samsung 960 NVMe SSDs to deliver groundbreaking performance over 12GB/s
- Micron accelerates all-flash storage speed, performance and value with new flexible petabyte-scale enterprise data center solution
- Qualcomm Snapdragon 660 and 630 mobile platforms drive advanced photography, enhanced gaming, integrated connectivity and machine learning