Titlebook: High Performance Embedded Architectures and Compilers; Fourth International André Seznec,Joel Emer,Theo Ungerer Conference proceedings 2009

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Accomodating Diversity in CMPs with Heterogeneous Frequencieses performance by 16% on average and saves CPU energy by up to 16% across the NAS and SPEC-OMP benchmarks on a quad-core AMD platform. Workload balancing via dynamic partitioning yields results within 5% of the overall ideal value. Finally, we show feasible methods to determine at run time whether u

Metastasis

High Performance Embedded Architectures and CompilersFourth International

Interlocking

Keynote: Challenges on the Road to Exascale Computingery have driven the development of more powerful systems. In 2008, we saw the arrival of the first petaflop machine, which quickly topped the Top500 list, while also occupying the number one position on the Green500 list. Historic trends indicate that in ten years, we should be at the exascale level

兽群

Keynote: Compilers in the Manycore Eraore associated with GPUs or with any hardware specialized co-processors usually offer a much higher peak performance/power ratio. When high performance and power efficiency has to be achieved, specialized hardware is often the way to go. Combining a general-purpose multicore with a highly parallel c

Ballerina

Steal-on-Abort: Improving Transactional Memory Performance through Dynamic Transaction Reorderingbe optimally ordered to minimise aborts, but such an ordering is often either complex, or unfeasible, to obtain..This paper introduces a new technique called ., which aims to improve transaction ordering at runtime. Suppose transactions A and B conflict, and B is aborted. In general it is difficult

整洁

Predictive Runtime Code Scheduling for Heterogeneous Architectures is a heterogeneous system. Combining the CPU and the GPU brings great amounts of processing power. However, such architectures are often used in a restricted way for domain-specific applications like scientific applications and games, and they tend to be used by a single application at a time. We e

LVAD360

Collective Optimizationo a necessity to gather a large number of runs often with the same data set and on the same environment in order to test many different optimizations and to select the most appropriate ones. Naturally, in many cases, users cannot afford a training phase, will run each data set once, develop new prog

Entrancing

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