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+ - NVIDIA SHIELD Specs Finalized, Pre-Orders To Begin May 20->

Submitted by bigwophh
bigwophh (1100019) writes "NVIDIA’s Android-based, portable gaming system and media streaming device, originally known as Project SHIELD, was a big hit at CES. NVIDA has since dropped "Project" from the name and it appears the device is about ready to ship. If you’re unfamiliar with SHIELD, it is essentially a game controller with a built-in, flip-up 5” multi-touch screen. It is powered by NVIDIA’s own Tegra 4 quad-core SoC (System-on-Chip) with ARM A15 CPU cores, 72 GPU cores, 2GB of RAM, 16GB of internal storage, 802.11n 2x2 MIMO Wi-Fi, Bluetooth 3.0, and GPS support, among a number of other features. In addition to offering an array of Tegra-optimized games, part of SHIELD’s allure is the ability to wirelessly stream games and other media from a GeForce GTX-powered PC to any TV connected to SHIELD. Pricing for the device is set at $349 and pre-sales begin on May 20."
Link to Original Source

Comment: Watch video of simulation of this collision (Score 5, Interesting) 217

by gupg (#40176785) Attached to: Andromeda On Collision Course With the Milky Way
NVIDIA showed a simulation of this collision running on their latest Tesla GPGPU based on the "Kepler" architecture

Starts at around 1:00 on this video with a great explanation of the collision itself.

(NVIDIA employee)

Comment: Re:Can someone tell me NVidia's business model? (Score 1) 89

by gupg (#38376918) Attached to: NVIDIA Releases Source To CUDA Compiler

Discrete graphics is going away, they seem to be leaning increasingly towards the HPC market but that is tiny compared to the consumer graphics market that their company was built on. I just don't see it. Anyone?

Discrete GPU market is growing. See JPR's analyst reports

here is the full report

Comment: Easy way to program GPUs (Score 1) 205

by gupg (#38258844) Attached to: Ask Slashdot: Parallel Cluster In a Box?
There is a new easier way to program GPUs now using Directives-based compilers.

Idea is that you add some high-level pragmas to your C or Fortran code that a parallelizing compiler
uses to map to the GPU accelerator. Of course, you have to expose parallelism in the code for
the compiler to do a decent job. Example, use more data-parallel data structures. But this is a nice
incremental way to take advantage of the GPU.

Check it out at:

NVIDIA - Tesla Group

Comment: Re:Well the other thing I'd say it shows (Score 1) 77

by gupg (#36590474) Attached to: AMD Gains In the TOP500 List
I think this is a big misconception about GPUs. They are good at many applications - not just Linpack.

Take a look at the list of applications ranging from video transcoding to weather forecasting to computational chemistry to physics at:

In fact, the researchers at the Chinese Academy of Sciences just ran one of the fastest scientific simulations using their GPU supercomputer (#2 on the Top500 list):

There are tons of papers at the Supercomputing conference for real "full" applications in a very diverse range of applications that are accelerated using GPUs.

Comment: Highlights as per Top500 site (Score 4, Interesting) 77

by gupg (#36580168) Attached to: AMD Gains In the TOP500 List
The top500 site has its own take on highlights:

- The two Chinese systems at No. 2 and No. 4 and the Japanese Tsubame 2.0 system at No. 5 are all using NVIDIA GPUs to accelerate computation, and a total of 19 systems on the list are using GPU technology.
- China keeps increasing its number of systems and is now up to 62, making it clearly the No. 2 country as a user of HPC, ahead of Germany, UK, Japan and France.
- Intel continues to provide the processors for the largest share (77.4 percent) of TOP500 systems. Intel’s Westmere processors increased their presence in the list strongly with 169 systems, compared with 56 in the last list.
- Quad-core processors are used in 46.2 percent of the systems, while already 42.4 percent of the systems use processors with six or more cores.
- Cray defended the No. 2 spot in market share by total against Fujitsu, but IBM stays well ahead of either. Cray’s XT system series remains very popular for big research customers, with three systems in the TOP 10 (one new and two previously listed).

In my opinion, the newest & most important trend in high performance computing is the advent of accelerators like GPUs.

Comment: CUDA C++ and Thrust (Score 2) 187

by gupg (#36458966) Attached to: Microsoft Demos C++ AMP At AMD Developers Summit
This is an awesome development - Microsoft adding support for GPU computing in their mainstream tools and C++.

Today, CUDA C++ already provides a full C++ implementation on NVIDIA's GPUs:

And the Thrust template library provides a set of data structures and functions for GPUs (similar in spirit to STL):

- biased NVIDIA employee

+ - China does it again->

Submitted by gupg
gupg (58086) writes "When China built the fastest supercomputer based on NVIDIA GPUs last year, a lot naysayers said this was just a stunt machine. Well, guess what — here comes the science! They are working on better material for solar panels and they ran the world's fastest simulation ever! NVIDIA (whose GPUs accelerate these applications as a co-processor) blogged on this a while ago, where they talk about how the US really needs to up its investment in high performance computing."
Link to Original Source

Comment: Lots of GPU-accelerated numerical packages (Score 1) 89

by gupg (#36225024) Attached to: Matlab Integrates GPU Support For UberMath Computation

There are tons of other CUDA accelerated numerical packages besides Matlab -- Mathematica, LabView, plugins / wrappers / libraries for Python, R, IDL. Some of these are linked from NVIDIA's website

Others from

Thrashing is just virtual crashing.