Recently, the introduction of graphics processing units (GPUs) has enabled the acceleration of computationally intensive processes with their massive parallel capabilities.
Numerous applications have been implemented for the acceleration of computationally-intensive problems in various computational science fields including bioinformatics, in which big data problems are prevalent.
This requires acceleration of the most intensive algorithms, found in the baseband, such as the FFT and Viterbi algorithms which are critical to overall performance.
On the other hand, due to the intensive fuzzy membership calculations involved, accelerating FCM is significantly more difficult, which is why the majority of existing acceleration methods involve approximations [56 60].
Calculations are intricate and computer-intensive, typically requiring detailed geometry models, sophisticated acceleration algorithms, high-performance parallel computations, and coupling of large and complex transport and activation codes and databases.
Most of the existing prediction algorithms are computationally intensive, and there is a clear need for acceleration.
Towards demonstrating the value of our solution, in the first case we show that there are no performance penalties in adopting IaaS acceleration for the majority of data intensive or IO bound applications.
"While the industry has taken steps to reduce emissions, the shift from mining to in-situ production, which is almost three times as emissions intensive as mining, is resulting in a continued acceleration of emissions from this sector".
BlueStacks' beta release uses your PC's graphics card to make apps running graphics-intensive engines like Unity run without glitches, using hardware acceleration not dissimilar to that which takes place in the browser.
The acceleration response behind the frame beam is more intensive than that at the back of the gravity wall.
