In the presence of on-chip multithreading, a Virtual Machine (VM) implementation can readily take advantage of service threads for enhancing performance by performing tasks such as profile collection and analysis, dynamic optimization, and garbage collection concurrently with program execution. In this context, a hardware-assisted profiling mechanism is proposed. The Relational Profiling Architecture (RPA) is designed from the top down. RPA is based on a relational model similar to the relational database model. Instructions selected for profiling produce a record of information. A simple query engine examines these records for patterns, and performs simple actions on matching records.

Virtual machine service threads can perform many tasks in parallel with program execution such as garbage collection, dynamic compilation, and profile collection and analysis. Hardware-assisted profiling is essential for providing service threads with needed information in a flexible and efficient way. A relational profiling architecture (RPA) is proposed for meeting this goal. The RPA selects particular instructions for profiling, and communicates collected information to service threads through shared memory message queues. The RPA's capabilities lead to new profiling applications, such as concurrent garbage collection. Simulations indicate that a low-cost implementation of the RPA should be able to profile four in-flight instructions simultaneously, and provide storage for eight profile records. Profiling overhead is less than 0.5% for concurrent garbage collection and edge profiling.

Branch predictors typically use combinations of branch PC bits and branch histories to make predictions. Recent improvements in branch predictors have come from reducing the effect of interference, i.e. multiple branches mapping to the same table entries. In contrast, the branch difference predictor (BDP) uses data values as additional information to improve the accuracy of conditional branch predictors. The BDP maintains a history of differences between branch source register operands, and feeds these into the prediction process. An important component of the BDP is a rare event predictor (REP) which reduces learning time and table interference. An REP is a cache-like structure designed to store patterns whose predictions differ from the norm. Initially, ideal interference-free predictors are evaluated to determine how data values improve correlation. Next, execution driven simulations of complete designs realize this potential. The BDP reduces the misprediction rate of five SPEC95 integer benchmarks by up to 33% compared to gshare and by up to 15% compared to Bi-Mode predictors.