Graphics Cards Guide

NVIDIA GTC 2012 Roundup - GPU Computing Momentum Grows Ever Stronger

GTC 2012: The End is Just the Beginning

GTC 2012 - GPU Computing Momentum Grows Ever Stronger

GP-GPU computing is just in its in infancy. The first GPU Technology Conference was held in 2008 (under the NVISION'08 branding) and it has gone from strength to strength. NVIDIA saw the future of GPU computing and threw its weight behind this momentum. The parallel processing engines inside GPUs make them ideal for a wide range of computing tasks where there is a massive need to calculate a string of similar data sets fast, removing them from their traditional roles in just graphical processing.

Fast forward four years to 2012 when GPU compute has advanced in terms of hardware and software developments. NVIDIA has since designed several new GPUs, most recent of which is the launch of Kepler whose architecture is the conerstone of power-efficient GPU computing platform. Kepler also has new features like Hyper-Q and Dynamic Parallelism to keep its performance ahead of its predecessor Fermi in the areas of GPU computing. Powering the new push for this year and the year ahead are the Tesla K10 and K20 GPUs which were unveiled in this conference and we spoke to the senior directors to understand its positioning and what it means to the research and scientific community. As the compute power of the GPUs grow ever higher, corresponding new services and features can be harnessed and that's exactly what we saw with GeForce GRID for using GPU processing capabilities over the cloud and other services.

In order to consolidate its foothold in the GP-GPU arena, NVIDIA created a more compute-friendly software development framework called CUDA. Its attempt to push GPU programming to higher level programming languages seemed to have borne fruit with CUDA in its fourth major revision, while its fifth revision (CUDA 5.0) is just on the horizon. NVIDIA labels its efforts in GPU computing as creating GPU computing ecosystems.

Such a label is somewhat appropriate as NVIDIA claims to have witnessed the organic growth in terms of the adoption of GPU computing by both commercial and non-commercial fronts. The company appears to be so confident of its foothold as we heard Dr. Sumit Gupta, Senior Director for Tesla GPU Computing, said that the main obstacle to the adoption of GPU-accelerated solution is inertia, and not competition from other companies.

That about sums up the momentum and technologies shared and uncovered during our visit to NVIDIA's GPU Technology Conference 2012. If you missed out on some of the articles we've published in detail, we've got them consolidated for your reading pleasure:-

Highlights GTC 2012
Highlights from Exhibit Hall of The GPU Tech Conference 2012

Highlights from Exhibit Hall of The GPU Tech Conference 2012

FEATURE / 18 May 2012

Apart from the numerous conference and break-out sessions happening during NVIDIA's Graphics Technology Conference 2012, there's also a huge hall of interesting exhibits from various participants vendors from emerging companies, to established industries. We bring to you some of the notable highlights from the show floor.

NVIDIA's GPU Computing Ecosystem - The Tesla Aspect

NVIDIA's GPU Computing Ecosystem - The Tesla Aspect

FEATURE / 18 May 2012

We had the chance to speak to Dr. Sumit Gupta, Senior Director for Tesla GPU Computing at one of the breakout sessions at GTC 2012. Read on to find out more about the hardware aspects of NVIDIA's GPU computing ecosystem.

Fireside Chat Session with NVIDIA CEO at GTC 2012

Fireside Chat Session with NVIDIA CEO at GTC 2012

NEWS / 18 May 2012

In true GTC tradition, NVIDIA CEO Huang sat down with US IT industry analyst Tim Bajarin and shared his views of the future for everything from mobile devices to cloud computing. He even explained why he abstains from participating in the social-networking phenomenon in private capacity.

GPU Computing in Science and Beyond

GPU Computing in Science and Beyond

FEATURE / 17 May 2012

From unlocking the crowd behavior of social animals to constellation simulation, GP-GPU computing is entrenching itself in realms of science in ways that were unthinkable just a few years ago. Read our highlights of the practical applications of GPU computing on the frontiers of science.

Spanning Physical Boundaries of GPU Computing with Kepler

Spanning Physical Boundaries of GPU Computing with Kepler

FEATURE / 17 May 2012

NVIDIA CEO Jen-Hsun Huang has revealed the cloud capabilities of its newly launched Kepler GPU. He also introduces a slew of hardware and software services built around this capability, effectively building high-performance computing ecosystems with the intention to democratize this technology.

Tesla K20 - Largest Silicon Chip So Far, Packed with over 7 Billion Transistors

Tesla K20 - Largest Silicon Chip So Far, Packed with over 7 Billion Transistors

NEWS / 16 May 2012

At GTC 2012, NVIDIA CEO Jen-Hsun Huang introduced two enterprise GPUs, the Telsa K10 and K20. The Telsa K10 is based on the GK104 core. The more powerful Tesla K20 features the GK110 core and according to CEO Huang, it is currently the most complex IC packed with 7.1 billion transistors.

Game Changing Announcement by NVIDIA at GTC 2012 - GeForce GRID

Game Changing Announcement by NVIDIA at GTC 2012 - GeForce GRID

NEWS / 16 May 2012

During his keynote address at GTC 2012, NVIDIA CEO Jen-Hsun Huang highlighted a new technology called GeForce GRID that enables the graphical prowess of the Kepler GPU to be harnessed as a cloud service.

NVIDIA Eclipse-Based IDE for GPU Computing on Linux and Mac OS Announced

NVIDIA Eclipse-Based IDE for GPU Computing on Linux and Mac OS Announced

NEWS / 15 May 2012

The world's first IDE for developing GPU accelerated applications on Linux and Mac OS-based systems, the NVIDIA Nsight provides debugging and profiling tools which allows HPC and graphics developers to fully optimize the performance of CPUs and GPUs.

CUDA Goes Open-Source, the LLVM Way

CUDA Goes Open-Source, the LLVM Way

NEWS / 10 May 2012

NVIDIA announces that open-source LLVM compiler now supports NVIDIA GPUs as a result of the company's contribution of its CUDA complier to the LLVM Project.