Paper Info
Title
Zorua: Enhancing Programming Ease, Portability, and Performance in GPUs by Decoupling Programming Models from Resource Management.
Abstract
The application resource specification--a static specification of several parameters such as the number of threads and the scratchpad memory usage per thread block--forms a critical component of the existing GPU programming models. This specification determines the performance of the application during execution because the corresponding on-chip hardware resources are allocated and managed purely based on this specification. This tight coupling between the software-provided resource specification and resource management in hardware leads to significant challenges in programming ease, portability, and performance, as we demonstrate in this work. Our goal in this work is to reduce the dependence of performance on the software-provided resource specification to simultaneously alleviate the above challenges. To this end, we introduce Zorua, a new resource virtualization framework, that decouples the programmer-specified resource usage of a GPU application from the actual allocation in the on-chip hardware resources. Zorua enables this decoupling by virtualizing each resource transparently to the programmer. We demonstrate that by providing the illusion of more resources than physically available, Zorua offers several important benefits: (i) Programming Ease: Zorua eases the burden on the programmer to provide code that is tuned to efficiently utilize the physically available on-chip resources. (ii) Portability: Zorua alleviates the necessity of re-tuning an applicationu0027s resource usage when porting the application across GPU generations. (iii) Performance: By dynamically allocating resources and carefully oversubscribing them when necessary, Zorua improves or retains the performance of applications that are already highly tuned to best utilize the resources. The holistic virtualization provided by Zorua has many other potential uses which we describe in this paper.
Year
Venue
Field
2018
arXiv: Distributed, Parallel, and Cluster Computing
Resource management,Virtualization,Programmer,Programming paradigm,Computer science,Scratchpad memory,Porting,Software portability,Hardware architecture,Distributed computing
DocType
Volume
Citations 
Journal
abs/1802.02573
0
PageRank 
References 
Authors
0.34
1
8
Name
Order
Citations
PageRank
Nandita Vijaykumar11467.55
Kevin Hsieh222310.93
Gennady Pekhimenko370628.75
Samira Manabi Khan459618.12
Ashish Shrestha501.01
Saugata Ghose671836.45
Phillip B. Gibbons76863624.14
Onur Mutlu89446357.40