Instead of sitting idle when not processing graphics, the GPU is constantly available to perform other tasks. Since GPUs are optimized for processing vector calculations, they can even process some instructions faster than the CPU. Since processors can complete millions of operations each second, data is often stored in the buffer only for a few milliseconds. The most popular is OpenCL , an open standard supported by multiple platforms and video cards.
If you would like to reference this page or cite this definition, you can use the green citation links above. The goal of TechTerms. We strive for simplicity and accuracy with every definition we publish. If a graphics card is compatible with any particular framework that provides access to general purpose computation, it is a GPGPU. The primary difference is that where GPU computing is a hardware component, GPGPU is fundamentally a software concept in which specialized programming and equipment designs facilitate massive parallel processing of non-specialized calculations.
GPGPU acceleration refers to a method of accelerated computing in which compute-intensive portions of an application are assigned to the GPU and general-purpose computing is relegated to the CPU, providing a supercomputing level of parallelism. While the automatic parallelization and dispatch of tasks to GPGPU is certainly cool, doing so with the automatic breadth and depth of Manifold is entirely unprecedented in a commercial software product.
That's a "fail safe" action by Manifold but one which can result in much longer computation time than expected given the slower performance of CPU cores in tasks that can be faster run in GPGPU. While bugs that cause failure of GPGPU code should, of course, either be eliminated during pre-production testing or become increasingly rare as they are eliminated in updates, the possibility of long-running GPGPU tasks will always remain because computation times depend upon the amount of data involved and the complexity of computation.
Manifold and GPGPU calculations in general are so fast that Windows rebooting the graphics stack due to longer running computations on GPGPU should be rare, so rare that such effects will never be encountered by almost all users.
There are ways of turning the watchdog service off in Windows, but doing so may result in the display becoming obviously less responsive when long-running GPGPU tasks are being executed. It also makes sense to expect that anyone running such sophisticated tasks that they intensively utilize GPGPU is unlikely to have only a single GPU in their systems, given the insignificant cost relatively speaking to configure a system with multiple GPUs.
But that ends up requiring a pragma directive for each such query. Such a strategy may be taking too much of a "belt and suspenders" approach to eliminating the possibility that Windows will interfere with long-running GPGPU calculations on the card used for displays, but it could be a useful plan if massive GPGPU utilization is expected.
Does GPU parallelism speed up everything? It only speeds up computational tasks that are big enough to be worth dispatching to the GPU. It does nothing at all for tasks that involve no computation. Consider a thought experiment, for example: suppose we want to copy a 10 GB file in Windows from one disk drive to another disk drive.
We could have a hundred GPUs in our system and the job will not go any faster, because it involves no computation. It simply involves moving bytes between disk drives, which is a task that basically requires waiting around for bytes to be read from a terribly slow, rotating disk platter and then written onto the destination, terribly slow disk platter.
Reading a large shapefile, for example, is not going to go any faster with GPU because that task also is all about waiting to get information off disk. There is no thought involved for the processor and no computation to speed up, just the very slow wait for bytes to come in from disk. In contrast, a complex calculation doing much sophisticated mathematics for every pixel in a raster data set quite likely will gain significant performance from using GPU.
The rule of thumb is that if the job is top-heavy with lots of computation, GPU parallelism will help. Modern CPUs will often provide eight, twelve or more cores which can execute complex calculations with astonishing speed. When all of a CPU's cores are engaged in a parallelized task by Manifold, a modern eight-core CPU providing sixteen hypercores can execute many tasks so fast that it will be done before the job could be dispatched to GPU.
For most people, there is not much point in buying the latest, most expensive GPU since even inexpensive GPUs are so fast the number of jobs that will go significantly faster on a super-expensive GPU are few and far between.
Even with very computationally intensive jobs usually a mid-range GPU is plenty. AMD and Intel both make fine products, including graphics chips. The former limit was difficult to hit: To hit the limit, the query had to have a SQL expression that was dispatched entirely to GPGPU and that expression had to be extremely large. The increase in memory size is aimed at future scenarios, to allow splitting operations into bigger chunks in the future, enabling use of very large memory for processing.
Actual code dispatched, of course, will be sized to memory available in whatever GPU cards are installed. GPU computation is so fast that very quickly other parts of the system will become the bottleneck, so it usually makes no sense to spend many thousands on one or more GPU cards to plug into a four core CPU machine that has limited memory and slow hard disk.
Specialized applications that do very intensive mathematical calculations, of course, will show greater differences sooner. Must I use Quadro or Tesla or other special brands? Can I mix various GPU generations and use multiple cards? Manifold will identify all GPGPU capable cards in the system and will take advantage of all of them, even older cards that may have fewer cores.
0コメント