João Lourenço

Software Transactional Memory algorithms associate metadata with the memory locations accessed during a transactions lifetime. This metadata may be stored in an external table and accessed by way of a function that maps the address of each memory location with the table entry that keeps its metadata (this is the out-place or external scheme); or alternatively may be stored adjacent to the associated memory cell by wrapping them together (the in-place scheme). In transactional memory multi-version algorithms, several versions of the same memory location may exist. The efficient implementation of these algorithms requires a one-to-one correspondence between each memory location and its list of past versions, which is stored as metadata. In this chapter we address the matter of the efficient implementation of multi-version algorithms in Java by proposing and evaluating a novel in-place metadata scheme for the Deuce framework. This new scheme is based in Java Bytecode transformation techniques and its use requires no changes to the application code. Experimentation indicates that multi-versioning STM algorithms implemented using our new in-place scheme are in average 6 × faster than when implemented with the out-place scheme.

This document describes a work-in-progress development of NGen-VM, a distributed infrastructure that manages execution environments with run-time and programming language support targeting applications developed in the Java programming language, deployed over clusters of many-core computers. For each running application or suite of related applications, a dedicated single-system image will be provided, regardless of the concurrent threads running on a single machine (on several cores) or scattered on different computers. Such system images rely on a single model for concurrency management (Transactional Shared Memory Model), in order fill the gap between the hardware infrastructure of clusters of many-core nodes and the application runtime that is independent from that hardware infrastructure. Interactions between threads in the same tasks will be supported by a Transactional Memory framework that provides the programming language with Atomic and Isolated code regions. Interactions between thread on different machines will also use the Transactional Memory model, but now resorting to a Distributed Shared Memory abstraction.

We describe our experimentation with the design and implementation of specific environments, consisting of heterogeneous computational, visualization, and control components. We illustrate the approach with the design of a problem–solving environment supporting the execution of genetic algorithms. We describe a prototype supporting parallel execution, visualization, and steering. A life cycle for the development of applications based on genetic algorithms is proposed.