Parallel computing in genomic research: advances and applications

Parallel computing in genomic research: advances and applications

Today's genomic experiments have to process the so-called "biological big data" that is now reaching the size of Terabytes and Petabytes. To process this huge amount of data, scientists may require weeks or months if they use their own workstations. Parallelism techniques and high-performance computing (HPC) environments can be applied for reducing the total processing time and to ease the mana...

Advances in Parallel Computing

State-of-the-art dense linear algebra software, such as the LAPACK and ScaLAPACK libraries, suffer performance losses on multicore processors due to their inability to fully exploit thread-level parallelism. At the same time the coarse-grain dataflow model gains popularity as a paradigm for programming multicore architectures. This work looks at implementing classic dense linear algebra workloa...

Advances in Research and Applications of Smart Hydrogels Part I: Preparation Methods and Classification

Advances in Soft Computing Techniques and Applications

between cities are unstable in time. The second paper, written by Nuser, presents a molecular-inspired computing approach to solve the classical three-partition problem. Besides the computational complexity, the approach is investigated in terms of specific elements, such as number of strands, tubes and the longest library strand. In the third paper, Singh et al. adopted the features of an ABC ...

Advances at a Glance in Parallel Computing

In the history of computational world, sequential uni-processor computers have been exploited for years to solve scientific and business problems. To satisfy the demand of compute & data hungry applications, it was observed that better response time can be achieved only through parallelism. Large computational problems were partitioned and solved by using multiple CPUs in parallel. Computing pe...