Heterogeneous computing: challenges and opportunities

A.A. Khokhar, V.K. Prasanna, M.E. Shaaban, C.-L. Wang
1993 Computer  
Anytime you work with oranges and apples, you'll need a number of schemes to organize total performance. This article surveys the challenges posed by H omogeneous computing, which uses one or more machines of the same type, has provided adequate performance for many applications in the past. Many of these applications had more than one type of embedded parallelism, such as single instruction, multiple data (SIMD) and multiple instruction, multiple data (MIMD). Most of the current parallel
more » ... rent parallel machines are suited only for homogeneous computing. However, numerous applications that have more than one type of embedded parallelism are now being considered for parallel implementation. On the other hand, as the amount of homogeneous parallelism in applications decreases, homogeneous systems cannot offer the desired speedups. To exploit the heterogeneity in computations, researchers are investigating a suite of heterogeneous architectures. Heterogeneous computing (HC) is the well-orchestrated and coordinated effective use of a suite of diverse high-performance machines (including parallel machines) to provide superspeed processing for computationally demanding tasks with diverse computing needs.' An HC system includes heterogeneous machines, high-speed networks, interfaces, operating systems, communication protocols, and programming environments, all combining to produce a positive impact on ease of use and performance. Figure 1 shows an example HC environment. Heterogeneous computing should be distinguished from network computing or high-performance distributed computing, which have generally come to mean either clusters of workstations or ad hoc connectivity among computers using little more than opportunistic load-balancing. HC is a plausible, novel technique for heterogeneous solving computationally intensive problems that have several types of embedded computing and parallelism. HC also helps to reduce design risks by incorporating proven technology and existing designs instead of developing them from scratch. However, discusses some several issues and problems arise from employing this technique, which we discuss. In the past few years, several technical meetings have addressed many of these approaches to opening issues. There is also a growing interest in using this paradigm to solve Grand up its opportunities. Challenges problems. Richard Freund has organized the Heterogeneous Processing Workshops held each year at the IEEE International Parallel Processing 18
doi:10.1109/2.214439 fatcat:a5wzxmuxfvhcthbx5jtxl4xj5i