The purpose of this research is to design efficient parallel algorithms with higher parallelism and lower data correlation, using methods such as decomposition and coordination, sparse vectors, Krylov subspace iterations, and so on [9, 10, 11, 12].
Two major advantages of DNA computing – huge memory capacity and high parallelism – are being explored for large-scale parallel computing, mass data storage and cryptography.
The proposed ISP full chain is designed to work with SIMD-style processingrocessing; however, due to its high parallelism, the proposed design can be used for platforms with other types of microprocessors such as Intel [31], ARM [32], and the TI Digital Signal Processor (DSP) [33].
Parallel design patterns have been recognised to have the potential to induce a radical change in the parallel programming scenario, such that new parallel program will be capable of exploiting the high parallelism provided by hardware vendors [ 32].
Our new QR factorization algorithm falls in the category of the tile algorithms which naturally enables good data locality for the sequential kernels executed by the cores (high sequential performance), low number of messages in a parallel distributed setting (small latency term), and fine granularity (high parallelism).
Our FPU is fast and efficient, due to the high parallelism of its architecture: the functional units inside the datapath can operate in parallel and independently from each other.
One way to reduce this long time of execution is to develop a parallel algorithm, using the advantages of the recent computer architectures that allow high parallelism.
Four integrated MFCs exhibited high parallelism, and independence without the cross contamination or electric signal interference.
