Traditional offline optimization frameworks rely on representative hardware, software, and inputs to compare different optimizations on. With application-specific optimization for mobile systems though, the idea of a representative testbench is ...

Iterative compilation based on machine learning can automatically predict the best optimization for the new programs. However, the efficient prediction models often require repetitive training, which leads to a higher training time overheads, and ...

Today's compilers have a plethora of optimizations-transformations to choose from, and the correct choice, order as well parameters of transformations have a significant/large impact on performance; choosing the correct order and parameters of ...

Optimizing programs to exploit the underlying hardware architecture is an important task. Much research has been done on enabling compilers to find the best set of code optimizations that can build the fastest and less resource-hungry executable for a ...

Memory wall is an important factor that influences program performance, and its alleviation relies on memory optimization of the program. Static approaches optimize memory performance based on analytical models that are hard to achieve because of ...

The performance gap for high performance applications has been widening over time. High level program transformations are critical to improve the applications' performance, many of which concern the determination of optimal values for transformation ...

The rapidly increasing number of architectural changes in embedded processors puts compiler technology under an enormous stress. This is emphasized by new demands on compilers, like requirements to reduce static code size, energy consumption or power ...