Introduction of CAA into a mathematics course for technology students to address a change in curriculum requirements

The mathematical requirements for engineering, science and technology students has been debated for many years and concern has been expressed about the mathematical preparedness of students entering higher education. This paper considers a mathematics course that has been specifically designed to address some of these issues for technology education students. It briefly chronicles the changes that have taken place over its lifetime and evaluates the introduction of Computer Assisted Assessment (CAA) into a course already being delivered using Computer Aided Learning (CAL). Benefits of CAA can be categorised into four main areas. 1. Educational – achieved by setting short, topic related, assessments, each of which has to be passed, thereby increasing curriculum coverage. 2. Students – by allowing them to complete assessments at their own pace removing the stress of the final examination. 3. Financial – increased income to the institution, by broadening access to the course. Improved retention rate due to self-paced learning. 4. Time – staff no longer required to set and mark exams. Most students preferred this method of assessment to traditional exams, because it increased confidence and reduced stress levels. Self-paced working, however, resulted in a minority of students not completing the tests by the deadline

SSA-Based Register Allocation with PBQP

Abstract. Recent research shows that maintaining SSA form allows to split register allocation into separate phases: spilling, register assign-ment and copy coalescing. After spilling, register assignment can be done in polynomial time, but copy coalescing is NP-complete. In this pa-per we present an assignment approach with integrated copy coalescing, which maps the problem to the Partitioned Boolean Quadratic Problem (PBQP). Compared to the state-of-the-art recoloring approach, this re-duces the relative number of swap and copy instructions for the SPEC CINT2000 benchmark to 99.6 % and 95.2%, respectively, while taking 19 % less time for assignment and coalescing

Bilingualism for the Minor or the Major? An Evaluative Analysis of Parallel Conceptions in China

This paper is an analysis of two conceptions of bilingualism that exist in parallel in China. One is traditional bilingualism referring to the use of a native minority language and standard Chinese by minority groups and the other, seen as bilingualism with modern characteristics, is a modern-day phenomenon in which the majority Han group aspire to produce bilinguals with a strong competence in mother tongue Chinese and a foreign language, primarily English, by using Chinese and the foreign language as mediums of instruction in teaching school subjects. The focus of the analysis is on the latter for the simple reason that current literature on the new phenomenon is mostly available only in Chinese. An equally important aim of this paper is to explore the impact of the new phenomenon on minority education and to examine the reason why this impact is largely ignored in bilingualism discussions, despite obvious consequences with respect to ethnic identity, personality development and academic performance of minority students. Thus, the traditional conception is briefly reviewed at the start

Hardware-Assisted Cross-Generation Prediction of GPUs under Design

This paper introduces a predictive modeling framework for GPU performance. The key innovation underlying this approach is that performance statistics collected from representative workloads running on current generation GPUs can effectively predict the performance of next-generation GPUs. This is useful when simulators are available for the next-generation device, but simulation times are exorbitant, rendering early design space exploration of microarchitectural parameters and other features infeasible. When predicting performance across three Intel GPU generations (Haswell, Broadwell, Skylake), our models achieved impressively low out-of-sample-errors ranging from 7.45% to 8.91%, while running 29 481 to 44 214 times faster than cycle-accurate simulations. A detailed ranking of the most impactful features selected for these models provides an insight as to which microarchitectural subsystems have the greatest impact on performance from one generation to the next

Hardware-Assisted Cross-Generation Prediction of GPUs under Design

This paper introduces a predictive modeling framework for GPU performance. The key innovation underlying this approach is that performance statistics collected from representative workloads running on current generation GPUs can effectively predict the performance of next-generation GPUs. This is useful when simulators are available for the next-generation device, but simulation times are exorbitant, rendering early design space exploration of microarchitectural parameters and other features infeasible. When predicting performance across three Intel GPU generations (Haswell, Broadwell, Skylake), our models achieved impressively low out-of-sample-errors ranging from 7.45% to 8.91%, while running 29 481 to 44 214 times faster than cycle-accurate simulations. A detailed ranking of the most impactful features selected for these models provides an insight as to which microarchitectural subsystems have the greatest impact on performance from one generation to the next

HALWPE: Hardware-Assisted Light Weight Performance Estimation for GPUs

This paper presents a predictive modeling framework for GPU performance. The key innovation underlying this approach is that performance statistics collected from representative workloads running on current generation GPUs can effectively predict the performance of next-generation GPUs. This is useful when simulators are available for the next-generation device, but simulation times are exorbitant, rendering early design space exploration of microarchitectural parameters and other features infeasible. When predicting performance across three Intel GPU generations (Haswell, Broadwell, Skylake), our models achieved low out-of-sample-errors ranging from 7.45% to 8.91%, while running 30,000-45,000 times faster than cycle-Accurate simulation