Scientific revolutions, progess and accounting research

Thomas Kuhn’s concept of a normal science paradigm has been utilised and criticised across a range of social science fields. However, Kuhn’s aim was to argue that science progresses not in an incremental manner but through a series of paradigms that need a revolution in thought to shift from one to the next. This paper addresses Kuhn’s work focusing on the totality of his model, but recognising the ambiguities concerning paradigm shifts that have led to charges of relativism. To address this weakness an argument is advanced for a political economy analysis of the publication process and the development of critical accounting research centred on human emancipation. The paper concludes with some suggested research agendas particularly relevant to the Irish context.

Prediction-Based Power-Performance Adaptation of Multithreaded Scientific Codes

Computing has recently reached an inflection point with the introduction of multicore processors. On-chip thread-level parallelism is doubling approximately every other year. Concurrency lends itself naturally to allowing a program to trade performance for power savings by regulating the number of active cores; however, in several domains, users are unwilling to sacrifice performance to save power. We present a prediction model for identifying energy-efficient operating points of concurrency in well-tuned multithreaded scientific applications and a runtime system that uses live program analysis to optimize applications dynamically. We describe a dynamic phase-aware performance prediction model that combines multivariate regression techniques with runtime analysis of data collected from hardware event counters to locate optimal operating points of concurrency. Using our model, we develop a prediction-driven phase-aware runtime optimization scheme that throttles concurrency so that power consumption can be reduced and performance can be set at the knee of the scalability curve of each program phase. The use of prediction reduces the overhead of searching the optimization space while achieving near-optimal performance and power savings. A thorough evaluation of our approach shows a reduction in power consumption of 10.8 percent, simultaneous with an improvement in performance of 17.9 percent, resulting in energy savings of 26.7 percent.