Abstract
Architects and applications scientists often use performance models to explore a multidimensional design space of architectural characteristics, algorithm designs, and application parameters. With traditional performance modeling tools, these explorations forced users to first develop a performance model and then repeatedly evaluate and analyze the model manually. These manual investigations proved laborious and error prone. More importantly, the complexity of this traditional process often forced users to simplify their investigations. To address this challenge of design space exploration, we extend our Aspen (Abstract Scalable Performance Engineering Notation) language with three new language constructs: user-defined resources, parameter ranges, and a collection of costs in the abstract machine model. Then, we use these constructs to enable automated design space exploration via a nonlinear optimization solver. We show how four interesting classes of design space exploration scenarios can be derived from Aspen models and formulated as pure nonlinear programs. The analysis tools are demonstrated using examples based on Aspen models for a three-dimensional Fast Fourier Transform, the CoMD molecular dynamics proxy application, and the DARPA Streaming Sensor Challenge Problem. Our results show that this approach can compose and solve arbitrary performance modeling questions quickly and rigorously when compared to the traditional manual approach.
Highlights
The design of generation Exascale computer architectures as well as their future applications is complex, uncertain, and intertwined
Aspen can generate all of these estimates without application source code or low-level architectural information like Register Transfer Level (RTL)
While a more detailed description of Aspen has been published elsewhere [1], we briefly provide an overview and illustrate its use on an example model for a 1D Fast Fourier Transform (FFT)
Summary
The design of generation Exascale computer architectures as well as their future applications is complex, uncertain, and intertwined. Relevant performance models need to describe a complex, multidimensional design space of algorithms, application parameters, and architectural characteristics. We designed Aspen (Abstract Scalable Performance Engineering Notation) [1], a domain specific language for structured analytical performance modeling, to allow scientists to construct, evaluate, verify, compose, and share models of their applications. Aspen specifies a formal language and methodology that allows modelers to quickly generate representations of their applications as well as abstract machine models. Aspen can generate all of these estimates without application source code or low-level architectural information like Register Transfer Level (RTL). This ability to cope with high levels of uncertainty distinguishes Aspen from simulators, emulators, and other trace-driven approaches
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: 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.
