Embedded multicore computing and applications

Abstract

INTRODUCTIONIn the past few years, multicore and many core systems have been utilized to accelerate the research domains, including image processing, molecular dynamics, astrophysics, financial simulation, computer tomography, quantum chemistry, bioinformatics, and many others. Similarly, many programming models, such as OpenMP, OpenCL, and CUDA, have been released to aid programmers to develop a parallel computing application by launching the cores or threads in parallel. To efficiently launch cores or threads, software framework plays an important role to make cores or threads collaborate with each other. Also, an efficient parallel algorithm can help to improve the performance of applications, especially real-time applications. Many successful applications with multicore and many core systems through these parallel algorithms have unveiled many scientific results, which encourage application developers to adopt novel multicore and many core system computing technologies. This journal contains 10 papers, each paper covering one particular application and methodology. All of these papers not only provide novel ideas and state of-the-art technologies in the field but also stimulate future research for multicore computing and their applications. THEMES OF THIS SPECIAL ISSUEThis special issue contains research papers addressing the state-of-the-art in multicore systems and applications. A set of carefully selected works was invited based on the original presentations at the 4th International Workshop on Embedded Multicore Computing and Applications in conjunction with the 16th IEEE International Conference on High Performance and Communications, which was held in Paris, France, 20-22 August 2014 [1]. The manuscripts tackle research on different topics, including multicore systems, parallel input/output (IO), and graphics processing unit (GPU) applications. The set of accepted papers is briefly described in the remaining parts of this section. Parallel IO and parallel programming modelsMore than 50% of file operations in the storage systems are metadata operations. In the scenarios of read-more and write-less of massive small files, present distributed file systems suffer from the unsatisfying performance and scalability of metadata service because of random disk I/O during metadata operations. Duan et al.[2] propose a highly efficient metadata management for cloud storage systems. They introduce a concept of channel, which is an independent data storage pipe by binding an independent CPU core to each physical disk. In addition, a multi-channel fast key-value storage engine is proposed to provide the extremely efficient performance for the underlying storage service, which takes full advantages of multicore processors and parallel disk I/O.Nowadays, technology of multicore processor has not only taken more and more markets but also brought revolution and unavoidable collision to the programming personnel. In the past, the tenacious semiconductor problems of operating temperature and power consumption limited the pe...