The race for Exascale Computing has naturally led computer architecture to transit from the multicore era and into the heterogeneous era. Many systems are shipped with integrated CPUs and graphics processing units (GPUs). Moreover, various applications need to utilize both CPUs and GPUs executive resources, as many of their unique features prove the significant importance and strengths of using each one of the process units PUs. Several research studies consider partitioning the applications, scheduling their execution and allocating them onto the PUs resources. They investigate the important role of optimization and tackle intelligently scheduled tasks on the combination of CPU/GPU architecture CPUs and GPUs cores in achieving the peace of performance and power consumption of Exascale Computing. In this paper, the evolution of heterogeneous computing architectures, the approaches, and challenges toward achieving Exascale Computing, and the various algorithms and techniques used to partition and scheduling tasks are all reviewed. The existing frameworks and runtime systems utilized to optimize performance and improve energy efficiency in desecrates and fused chips in order to attain the objectives of Exascale Computing will also be reviewed.
The heterogeneous nature of Graphics processor unit (GPU) -CPU makes it a candidate for coming exascale systems.The cores of GPGPU-which is a cost-effective computing platform-are characterized by long periods of inactive times, which results in the underutilization of the hardware resources. This is due to several factors like the limitation of on-chip memory and register files, the inefficient scheduling mechanisms, and communication bottlenecks GPU -CPU communication. In order to counteract the underutilization of recourses, certain techniques have been proposed. In this research, many architectural and system-level techniques aiming to manage and fully leverage GPU resources are surveyed, compared and evaluated. Also, the significance and challenges of warp scheduler in GPUs are thoroughly discussed. The main purpose of this paper is to provide researchers an insight into warp scheduler techniques for GPUs, as well as motivate them to present more efficient methods for enhance performance via improve thread scheduler in future GPUs.
The race for Exascale Computing has naturally led computer architecture to transit from the multicore era and into the heterogeneous era. Exascale Computing within the heterogenous environment necessarily use the best-fit scheduling and resource utilization improvement. Task scheduling is the main critical aspect in managing the challenges of Exascale in the heterogenous computing environment. In this paper, a Communication and Computation Aware task scheduler framework (CCATSF) is introduced. The CCATSF framework consists of four parts; the first of which is the resource monitor, the second is the resources manager, the third is the task scheduler and the fourth is the dispatcher. The framework is based on a new hybrid task scheduling algorithm for a heterogenous computing environment. Our results are based on the random job generator that we implemented, and they indicate that the CCATSF framework, based on the proposed dynamic variant heterogenous early finish time (DVR-HEFT) algorithm is able to reduce the scheduler's makespan and increase the efficiency without increasing the algorithm's time complicity.
The heterogeneous Computing (HC) is the promised paradigm for high performance computing. In HC the vastly different architectures and programming models of each type of the computing unit, present several challenges in achieving collaborative computing. Task scheduling is the main critical aspect in managing these challenges. In this paper, a Communication and Computation Aware task scheduler framework (CCATSF) is introduced. The proposed task scheduling framework consist of four parts; the first of which is the resource monitor, the second is the resources manager, the third is the task scheduler and the fourth the dispatcher. We also introduce DVR-HEFT algorithm a new hybrid task scheduling algorithm, on which the framework is based. Our results indicate that CCATSF framework based on algorithm is able to reduce the scheduler's makespan without increasing the algorithm's time complicity.
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