Although the Java bytecode has numerous advantages, it also has certain shortcomings such as its slow execution speed and difficulty of analysis. In order to overcome such disadvantages, a bytecode analysis and optimization must be performed. The control flow of the bytecode should be analyzed; next, information is required regarding where the variables are defined and used to conduct a dataflo...

The static single-assignment (SSA) form of a program provides data flow information in a form which makes some compiler optimizations easy to perform. In this paper we present a new, simple method for converting to SSA form, which produces correct solutions for nonreducible control-flow graphs, and produces minimal solutions for reducible ones. Our timing results show that, despite its simplici...

preston briggs, keith d. cooper, timothy j. harvey and l. taylor simpson Tera Computer Company, 2815 Eastlake Ave. East, Seattle, WA 98102, USA (email: prestonKtera.com) Department of Computer Science, Rice University, 6100 S. Main MS 132, Houston, TX 77005, USA (email: keith,harvKrice.edu) Trilogy Development Group, 6034 W. Courtyard Dr., Austin, TX 78730, USA (email: Taylor SimpsonKtriology.com)

Widespread use of massive data and increasing reliance on space-based infrastructure are two of the emerging megatrends of the early 21 century. However, both come with liabilities: movement and processing of large data volumes is very expensive, and reliance on space-based infrastructure adds risk that problems in the still incompletely-understood space domain might cause work disruptions. Exo...

We present a simple SSA construction algorithm, which allows direct translation from an abstract syntax tree or bytecode into an SSA-based intermediate representation. The algorithm requires no prior analysis and ensures that even during construction the intermediate representation is in SSA form. This allows the application of SSA-based optimizations during construction. After completion, the ...

This chapter demonstrates an application of agent-based selection dynamics to the traffic assignment problem. We introduce an evolutionary dynamic approach that acquires payoff data from multi-agent reinforcement learning to enable a adaptive optimization of traffic assignment, provided that classical theories of traffic user equilibrium pose the problem as one of global optimization. We then s...