GPU Computing

What is gpu computing?


GPU computing is the use of a GPU (graphics processing unit) together with a CPU to accelerate general-purpose scientific and engineering applications. Pioneered five years ago by NVIDIA, GPU computing has quickly become an industry standard, enjoyed by millions of users worldwide and adopted by virtually all computing vendors.  GPU computing offers unprecedented application performance by offloading compute-intensive portions of the application to the GPU, while the remainder of the code still runs on the CPU. From a user’s perspective, applications simply run significantly faster. CPU + GPU is a powerful combination because CPUs consist of a few cores optimized for serial processing, while GPUs consist of thousands of smaller, more efficient cores designed for parallel performance. Serial portions of the code run on the CPU while parallel portions run on the GPU.


Most customers can immediately enjoy the power of GPU computing by using any of the GPU-accelerated applications listed in Nvidia’s application catalog, which highlights over one hundred, industry-leading applications. For developers, GPU computing offers a vast ecosystem of tools and libraries from major software vendors.


GPU computing momentum is growing faster than ever before. Today, some of the fastest supercomputers in the world rely on GPUs to advance scientific discoveries; 600 universities around the world teach parallel computing with NVIDIA GPUs; and hundreds of thousands of developers are actively using GPUs.


INtroducing the Compass gp series by technologies For Tomorrow


Technologies For Tomorrow, Inc. is pleased to offer the Compass GP series, a wide range of GP-GPU Computing offerings that include Clusters, Servers, and Workstations based off of the Nvidia line of GPUs.


Technologies For Tomorrow, Inc. works with our customers to provide systems that increase productivity by scaling from 8 or 16 CPU cores to thousands of GPU processor cores to solve large-scale problems by splitting the problem across multiple GPUs, greatly reducing overall processing times. The Compass GP series leverages low voltage core computing options as well as the latest 1-way, 2-way, and 4-way Intel and AMD processor platforms.


We are pleased to introduce our latest Compass GP parallel computing solutions powered by the NVIDIA Tesla K20, K10 and T20 series GPU platforms, based on the Tesla K20/K10 “Kepler” and T20 “Fermi” CUDA massively parallel architecture.


Why choose tesla gpus?


The NVIDIA CUDA parallel computing platform is enabled on GeForce, Quadro, and Tesla products. Whereas GeForce and Quadro are designed for consumer graphics and professional visualization, respectively, the Tesla product family is designed ground-up for parallel computing and programming and offers exclusive High performance computing features such as:


Full double precision floating point performance

  • 1.31 TFlops on the Tesla K20X

  • Higher double precision than consumer product

Higher performance on technical applications with large data sets

  • Larger on-board memory (6GB on a Tesla K20X and 8GB on a Tesla K10 GPU)

Faster communication with InfiniBand using NVIDIA GPUDirect

  • Special Linux patch, InfiniBand driver, and CUDA driver

ECC protection for uncompromised data reliability

  • For memories inside the GPU and the external GDDR5 memory

Cluster management and GPU monitoring software

  • GPU temperature monitoring, fan speed, and power

  • Exclusive access to GPUs in a cluster



Technologies For Tomorrow has the training and experience necessary to provide cutting edge and reliable GP-GPU computing systems that can take your data sets and applications to the next level. From GeForce to Tesla, we have systems that can stay within budget while also proving you the computation solution you need.  For more information please contact one of our sales associates to see what Technologies For Tomorrow Compass GP series can do for you.