![]() ![]() While it’s fine to test NMath Premium on any NVIDIA, testing on inexpensive consumer grade video cards will rarely show any performance advantage. However the “GTX” cards in the GeForce series generally perform well, as do the Quadro Desktop Produces and the Tesla cards. The “NVS” class of NVIDIA GPU’s (such as the NVS 5400M) generally perform very poorly as do the “GT” cards in the GeForce series. ![]() While many of NVIDIA’s GPU’s provide a good to excellent computational advantage over the CPU, not all of NVIDIA’s GPU’s were designed with general computing in mind. In the following, after introducing the new GPU bridge architecture, we’ll discuss each of these features separately with code examples.īefore getting started on our NMath Premium tutorial it’s important to consider your test GPU model. Yet the programmer can easily take as much control as needed to route executing threads or tasks to any available GPU device. Per-thread control for binding threads to GPU’s.Īs with the first release of NMath Premium, using NMath to leverage massively-parallel GPU’s never requires any kernel-level GPU programming or other specialized GPU programming skills.Automatic tuning of the CPU–GPU adaptive bridge to insure optimal hardware usage.The adaptive GPU bridge API in NMath Premium 6.0 includes the following important new features. This blog post will focus on the new API for doing computation on GPU’s with NMath Premium. The most recent release of NMath Premium 6.0 is a major update which includes an upgraded optimization suite, now backed by the Microsoft Solver Foundation, a significantly more powerful GPU-bridge architecture, and a new class for cubic smoothing splines. ![]()
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