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Because the GPU has access to every draw operation, it can analyze data in these forms quickly, whereas a CPU must poll every pixel or data element much more slowly, as the speed of access between a CPU and its larger pool of random-access memory (or in an even worse case, a hard drive) is slower than GPUs and video cards, which typically ...
Note: A new term "Quality of Silicon" (QoS) [1] is being promoted by the EDA industry in an attempt to measure the performance of backend EDA tools in isolation from the human designer's own performance in the frontend design stage. It is claimed that for historical reasons QoR is, and should remain, a measure of frontend design performance ...
A graphical demo running as a benchmark of the OGRE engine. In computing, a benchmark is the act of running a computer program, a set of programs, or other operations, in order to assess the relative performance of an object, normally by running a number of standard tests and trials against it.
In software engineering, performance testing is, in general, conducted to determine how a system performs in terms of responsiveness and stability under a particular workload. It can also serve to investigate, measure, validate, or verify other quality attributes of the system, such as scalability, reliability, and resource usage.
CUDA is a software layer that gives direct access to the GPU's virtual instruction set and parallel computational elements for the execution of compute kernels. [6] In addition to drivers and runtime kernels, the CUDA platform includes compilers, libraries and developer tools to help programmers accelerate their applications.
Graphics cards are sometimes called discrete or dedicated graphics cards to emphasize their distinction to an integrated graphics processor on the motherboard or the central processing unit (CPU). A graphics processing unit (GPU) that performs the necessary computations is the main component in a graphics card, but the acronym "GPU" is ...
The previous benchmarks are not suitable for testing parallel computers, [8] and the so-called Linpack's Highly Parallel Computing benchmark, or HPLinpack benchmark, was introduced. In HPLinpack the size n of the problem can be made as large as it is needed to optimize the performance results of the machine.
By combining locality, bandwidth, and different parallelization paradigms into a single performance figure, the model can be an effective alternative to assess the quality of attained performance instead of using simple percent-of-peak estimates, as it provides insights on both the implementation and inherent performance limitations.