A survey of techniques for warp scheduling in GPUs

Abstract

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.

Communication and Computation Aware Task Scheduling Framework Toward Exascale Computing

“…Hence, power efficiency must go up to 25 times of the 2013 range [3]. This efficiency is derived from process technology, better hardware and software architecture and circuits, in addition to utilizing, parallelize and improving the thread from 10 7 to 10 10 [3,4].…”

“…This way, we can benefit from the advantages of each and leverage the intelligent combination of both so as to achieve exascale performance and power consumption. Heterogeneous computing systems (HCS) are considered by many researchers the Exascale Computing system outrigger [3,4]. In an HCS, a various types of computing nodes, that are characterized by unrelated capabilities and equipped with spectrum types of computation units, are all interconnected via a highspeed network.…”

“…The efficient scheduling framework is capable of partitioning a job into small tasks, scheduling them on the HCS processing units in an efficiency way which achieves the minimum time-span and uses resources efficiently, in order for the job to be executed [3,4]. Mapping the tasks into the best-fit computing resources is the aim of task scheduling and allocating algorithms.…”

“…Mapping the tasks into the best-fit computing resources is the aim of task scheduling and allocating algorithms. As we note in [3,4], task scheduling is the mean critical aspect in managing these challenges. Also we found that the inappropriate scheduling of tasks on the computing resources offsets the profit of parallelization.…”

Communication and Computation Aware Task Scheduling Framework for Heterogeneous Computing

“…Much scientific research ever more Needs the use of massive computation-intensive applications that increasingly require highperformance computing systems (HPCS) effective and timely execution. Many researchers consider heterogeneous computing system (HCS) as the outrigger for the new generation of HPCS [1,2] . In an HCS, a number of compute devices will all be interconnected via a high-speed network, distinguished by different capabilities and fitted with specific computing units.…”