A generator of efficient abstract machine implementations and its application to emulator minimization

The implementation of abstract machines involves complex decisions regarding, e.g., data representation, opcodes, or instruction specialization levéis, all of which affect the final performance of the emulator and the size of the bytecode programs in ways that are often difficult to foresee. Besides, studying alternatives by implementing abstract machine variants is a time-consuming and error-prone task because of the level of complexity and optimization of competitive implementations, which makes them generally difficult to understand, maintain, and modify. This also makes it hard to genérate specific implementations for particular purposes. To ameliorate those problems, we propose a systematic approach to the automatic generation of implementations of abstract machines. Different parts of their definition (e.g., the instruction set or the infernal data and bytecode representation) are kept sepárate and automatically assembled in the generation process. Alternative versions of the abstract machine are therefore easier to produce, and variants of their implementation can be created mechanically, with specific characteristics for a particular application if necessary. We illustrate the practicality of the approach by reporting on an implementation of a generator of production-quality WAMs which are specialized for executing a particular fixed (set of) program(s). The experimental results show that the approach is effective in reducing emulator size.

Leveraging heterogeneity for energy minimization in data centers

Energy consumption in data centers is nowadays a critical objective because of its dramatic environmental and economic impact. Over the last years, several approaches have been proposed to tackle the energy/cost optimization problem, but most of them have failed on providing an analytical model to target both the static and dynamic optimization domains for complex heterogeneous data centers. This paper proposes and solves an optimization problem for the energy-driven configuration of a heterogeneous data center. It also advances in the proposition of a new mechanism for task allocation and distribution of workload. The combination of both approaches outperforms previous published results in the field of energy minimization in heterogeneous data centers and scopes a promising area of research.