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Andrea binder dissertation

Andrea binder dissertation

Andrea Binder. The global politics of tax haven regulation. Supervisor: Dr Helen Thompson. Magdalene College. Email: [email protected] Andrea Binder is a non-resident fellow with the Global Public Policy Institute Her PhD studies focus on the relationship between offshore finance – as tax.

On the one hand, fine-grained tasks i. On the other hand, coarse-grained tasks i.

We focus on task-parallel applications running in a single Java Virtual Machine on a shared- memory multicore. Despite their performance may considerably depend on the granularity of their tasks, this topic has received little attention in the literature.

Each delft should be in the form of a dissertation proposal that shares the nature of the problem to be researched, relevant andrea binder dissertation, hypotheses to be bad, method, analysis, and indicative references. Documents must be formatted in MS Word, weakening-spaced, using Times New Roman point font with 1 inch margins all around. Inaccuracies are limited to 15 pages, including all figures, tables, and capabilities and should begin with an abstract of goods or less.

Our work fills this gap, analyzing and optimizing the task granularity of such applications. In this dissertation, we present a new methodology to accurately and efficiently collect the granularity of each executed task, implemented in a novel profiler.

Our profiler collects carefully selected metrics from the whole system stack with low overhead.

Our tool helps developers locate performance and scalability https://edu-essay.top/2c/2990-sue-klebold-essay.php, and identifies classes and methods where optimizations related to task granularity are needed, guiding andrea binders dissertation towards useful optimizations. Moreover, we introduce a novel technique to drastically reduce the overhead of task-granularity profiling, by reifying the class hierarchy of the target application within a separate instrumentation process.

Our approach allows the instrumentation process to instrument only the classes representing tasks, inserting more efficient instrumentation code which decreases the overhead of task detection.

Our technique significantly speeds up task-granularity profiling and so enables the collection of accurate metrics with low overhead. We reveal inefficiencies related to fine-grained and coarse-grained tasks in several workloads. We demonstrate that the collected task-granularity profiles are actionable by optimizing task granularity in numerous benchmarks, performing optimizations in classes and methods indicated by our tool.

Our optimizations result in significant speedups up to a factor of 5. Our results highlight the importance of analyzing and optimizing task granularity on the Java Virtual Machine.

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