|NVIDIA GPU Computing & CUDA FAQ|
|Articles - Featured Guides|
|Written by Olin Coles and NVIDIA|
|Monday, 16 June 2008|
Page 1 of 5
NVIDIA GPU Compute FAQ
GPU Computing Overview
You are going to see an increased interest in GPU computing very soon. Terms such as "heterogeneous computing" and "parallel computing" are going to be used as often as the term "video card" is used in a product review. You won't want to miss this evolution in graphics technology, because we are witness to a pivital moment in time when computers are going to stop being filled with familiar single-purpose hardware. Benchmark Reviews offers this FAQ to help our readers understand what is happening, and help introduce them to what is coming. We don't want anyone to be left in the cold when the rest of the world learns how the GPU is learning to be a CPU.
Think of this as the moment when unibody construction evolved the automobile industry decades ago, and later shaped an entirely new dimension for manufacturers to approached building cars. We're experiencing the same moment, because the CPU is about to be joined by a GPU that does many of the same tasks; only better. For years, CPU manufacturers have enjoyed a position at the head of the table. But with heterogeneous computing now a present-day reality, many systems operate with smaller purpose-driven chips on a platform more representative of a round table.
Benchmark Reviews offers this FAQ to help our readers understand what is happening within our world of technology, and help introduce them to what is coming as we launch the NVIDIA GeForce GTX 280 Compute Video Card. We don't want anyone to be left in the cold when the rest of the world learns that the GPU is this years CPU.
What is heterogeneous computing?
Heterogeneous computing is the idea that to attain the highest efficiency applications should use both of the major processors in the PC: the CPU and GPU. CPUs tend to be best at serial operations with lots of branches and random memory access. GPUs, on the other hand, excel at parallel operations with lots of floating point calculations. The best result is achieved by using a CPU for serial applications and a GPU for parallel applications. Heterogeneous computing is about using the right processor for the right operation.
What kind of applications are serial, what kinds are parallel?
Very wew applications are purely serial or purely parallel. Most require both types of operations to varying degrees. Compilers, word processors, Web browsers, and e-mail clients are examples of applications that are primarily serial. Video playback, video encoding, photo processing, scientific computing, physics simulation, and 3D graphics (raytracing and rasterization) are examples of parallel applications.
What GPUs does CUDA operate with?
NVIDIA CUDA-enabled products can help accelerate the most demanding tasks-from video and audio encoding to oil and gas exploration, product design, medical imaging, and scientific research. Many CUDA programs require at least 256 MB of memory attached to the GPU. Please check your system's specifications to ensure the GPU has enough memory to run CUDA programs.
GPU Computing is a standard feature in NVIDIA's 8-Series and future GPUs. CUDA will be supported across a range NVIDIA GPUs although we recommend that the GPU have at least 256 MB of graphics memory. System configurations with less than the recommended memory size may not have enough memory to properly support CUDA programs.
What makes the GeForce GTX 280 a great parallel processor for the PC?
There are three key ingredients: