Abstract
Businesses and Academics are increasingly turning to Infrastructure as a Service (IaaS) Clouds such as Amazon's Elastic Compute Cloud (EC2) to fulfill their computing needs. Unfortunately, current IaaS systems provide a severely restricted pallet of rentable computing options which do not optimally fit the workloads that they are executing. We address this challenge by proposing and evaluating a manycore architecture, called the Sharing Architecture, specifically optimized for IaaS systems by being reconfigurable on a sub-core basis. The Sharing Architecture enables better matching of workload to micro-architecture resources by replacing static cores with Virtual Cores which can be dynamically reconfigured to have different numbers of ALUs and amount of Cache. This reconfigurability enables many of the same benefits of heterogeneous multicores, but in a homogeneous fabric, and enables the reuse and resale of resources on a per ALU or per KB of cache basis. The Sharing Architecture leverages Distributed ILP techniques, but is designed in a way to be independent of recompilation. In addition, we introduce an economic model which is enabled by the Sharing Architecture and show how different users who have varying needs can be better served by such a flexible architecture. We evaluate the Sharing Architecture across a benchmark suite of Apache, SPECint, and parts of PARSEC, and find that it can achieve up to a 5x more economically efficient market when compared to static architecture multicores. We implemented the Sharing Architecture in Verilog and present area overhead results.
Published Version (Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.