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Vuduc, Richard

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Qameleon: Hardware/software cooperative automated tuning for heterogeneous architectures

2013-08 , Kim, Hyesoon , Vuduc, Richard

The main goal of this project is to develop a framework that simplifies programming for heterogeneous platforms. The framework consists of (i) a runtime system to generate code that partitions and schedules work among heterogeneous processors, (ii) a general automated tuning mechanism based on machine learning and (iii) performance and power modeling techniques and profiling techniques to aid code generation.

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Algorithms and software with turnable parallelism

2010-09-30 , Vuduc, Richard

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How much (execution) time and energy does my algorithm cost?

2012-08-24 , Vuduc, Richard

When designing an algorithm or performance-tuning code, is time-efficiency (e.g., operations per second) the same as energy-efficiency (e.g., operations per Joule)? Why or why not? To answer these questions, we posit a simple strawman model of the energy to execute an algorithm. Our model is the energy-based analogue of the time-based "roofline" model of Williams, Patterson, and Waterman (Comm. ACM, 2009). What do these models imply for algorithm design? What might computer architects tell algorithm designers to help them better understand whether and how algorithm design should change in an energy-constrained computing environment?

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A Roofline Model of Energy

2012 , Choi, Jee Whan , Vuduc, Richard

We describe an energy-based analogue of the time-based roofline model of Williams, Waterman, and Patterson (Comm. ACM, 2009). Our goal is to explain—in simple, analytic terms accessible to algorithm designers and performance tuners—how the time, energy, and power to execute an algorithm relate. The model considers an algorithm in terms of operations, concurrency, and memory traffic; and a machine in terms of the time and energy costs per operation or per word of communication. We confirm the basic form of the model experimentally. From this model, we suggest under what conditions we ought to expect an algorithmic time-energy trade-off, and show how algorithm properties may help inform power management.

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