Summer Research Outcome
As my previous blog post concluded we had decided upon a selection of algorithms to try out. These were:
- Trees
- Lazy (kNN)
- Neural Networks
- Voting
Posts from the ‘Machine Learning’ Category
Explicit Ordering of Compiler Optimisations
Choosing the right compiler options can drastically reduce the energy consumption of a program. Finding the right way to schedule those options can give equally significant gains. Modern compilers use pass managers to schedule the order in which optimisations are applied, but these aim to reduce some combination of compile time and execution time rather than energy efficiency. I am interested in the task of learning how to predict which sequences of compiler optimisations will minimise energy consumption of a given executable on a given machine. Before this problem can be tackled it is first necessary to obtain data on how performance metrics (such as execution time and energy consumption) are affected by the ordering of optimisations for various benchmarks on various architectures. Part of the Mageec project involves the collection of this data and its storage in a relational database. In this article, I examine a number of relational representations that could be used for this purpose and explain the rationale behind the most promising approach.
Machine Learning for Energy Optimisations
My name is Munaaf Ghumran, and I am a third year student at Bristol University working on the Machine Learning analysis for MAGEEC.
Finding the Best Flags for a Given Program
The Machine Learning will be used to automatically predict the best set of flags for a given program, based on previously learnt data.
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