. These processes include generalization, discrimination, and strengthening of productions. Comparisons are made to similar concepts from past learning theories. How these learning mechanisms apply to produce the power law speedup in processing time with practice is discussed. It requires at least 100 hours

Software distributed-shared-memory (DSM) systems provide a desirable target for parallelizing compilers due to their flexibility. However, studies show synchronization and load imbalance are significant sources of overhead. In this paper, we investigate the impact of compilation techniques

Software distributed-shared-memory (DSM) systems provide a desirable target for parallelizing compilers due to their flexibility. However, studies show synchronization and load imbalance are significant sources of overhead. In this paper, we investigate the impact of compilation techniques

submitted to it at any time (with certain constrains depending on the machine). Con-sequently, it must have a sophisticated general purpose Operating System (OS) to schedule, dis-patch, maintain and monitor the tasks and assist them in special cases (particularly communica-tion and synchronization between

Current parallelizing compilers cannot identify a significant fraction of parallelizable loops because they have complex or statically insufficiently defined access patterns. As parallelizable loops arise frequently in practice, we advocate a novel framework for their identification: speculatively

: classical compiler optimisations, deforestation, partial evaluation and supercompilation. Mostly these give only linear speedups. (There are, however, two Refal-based exceptions: the supercompiler work by Turchin, and Nemytykhs supercompiler SCP4.) The limitation to linear time improvement has been proven

-Free Register Allocation. – We introduce the ELS1 algorithm which extends ELS0 to obtain a greedy algorithm for the problem of Register Allocation with Total Spills. – Finally, we present experimental results to compare the Graph Coloring and Extended Linear Scan algorithms. Our results show that the compile-time

. It enables the system to learn general rules. The second one is compiled and uses a specialized knowledge representation. It enables the system to solve problems rapidly and to detect when learning can occur in order to decide to call the first problem solver. To speedup rules, we use two mechanisms

extracted at compile-time to synthesize new operations which augment the functionality of a core processor. By integrating adaptation into a general-purpose computer, one can not only reap the performance benefits of applicationspecific processors, but also retain the general-purpose nature

This paper presents an algorithm that combines traditional EBL techniques and recent developments in inductive logic programming to learn effective clause selection rules for Prolog programs. When these control rules are incorporated into the original program, significant speed-up may be achieved