![]() Massive parallelism is precisely where modern graphics cards come into play since their architecture is primarily constructed to optimally support graphics calculations that are inherently parallel since they were originally build to parallelize graphical calculations at the level of vertices of 3D meshes as well as at the level of pixels. Early graphics boards were optimized to support 3D standards like OpenGL and DirectX (used mainly to implement 3D games) used fixed processing pipelines, i.e. it would benefit from the possibility to run thousands of sub-problems concurrently. Massive parallelism would be very advantageous in neuroimaging since many algorithms can be parallelized at the level of voxels and vertices, i.e. 2 or 4 cores in current laptops and a few processors with 4 or 8 cores on high-end workstations. Massive parallelism has, however, not yet achieved with this approach since the number of processors and cores in standard computers is limited, i.e. the computational routines are coded in a way that sub-problems can be run concurrently. To benefit from multiple processors and multiple cores, software has to be "multi-threaded", i.e. Increasing the number of processor cores is one of the major approaches to improve the computational power of modern processors since the clock speed can no longer increase substantially as in previous decades. Since many years, BrainVoyager uses central processing unit (CPU) based parallelization to speed up performance by exploiting the compute power of multiple processors and multiple processor cores. BrainVoyager v22.0 Exploiting the Power of GP-GPUs Background
0 Comments
Leave a Reply. |