HEP@home: A Volunteer Computing Project to Run Fast Simulation with Delphes for CEPC

Delphes is a C++ framework to perform a fast multipurpose detector response simulation. The Circular Electron Positron Collider (CEPC) experiment runs fast simulation with a modified Delphes based on its own scientific objectives. The CEPC fast simulation with Delphes is a High Throughput Computing (HTC) application with small input and output files. Besides, to compile and run Delphes, only ROOT software is necessary.Therefore, all these features make it appropriate to run CEPC fast simulation as a Volunteer Computing application. As a result, a BOINC project named HEP@home is developed to run fast simulation with Delphes for CEPC. This paper describes the internal structure of the project, pre and post data operations, and its development status

Engineering self-awareness with knowledge management in dynamic systems: a case for volunteer computing

The complexity of the modem dynamic computing systems has motivated software engineering researchers to explore new sources of inspiration for equipping such systems with autonomic behaviours. Self-awareness has recently gained considerable attention as a prominent property for enriching the self-adaptation capabilities in systems operating in dynamic, heterogeneous and open environments. This thesis investigates the role of knowledge and its dynamic management in realising various levels of self-awareness for enabling self­adaptivity with different capabilities and strengths. The thesis develops a novel multi-level dynamic knowledge management approach for managing and representing the evolving knowledge. The approach is able to acquire 'richer' knowledge about the system's internal state and its environment in addition to managing the trade-offs arising from the adaptation conflicting goals. The thesis draws on a case from the volunteer computing, as an environment characterised by openness, heterogeneity, dynamism, and unpredictability to develop and evaluate the approach. This thesis takes an experimental approach to evaluate the effectiveness of the of the dynamic knowledge management approach. The results show the added value of the approach to the self-adaptivity of the system compared to classic self­adaptation capabilities

Computational Methods to Advance Phylogenomic Workflows

Phylogenomics refers to the use of genome-scale data in phylogenetic analysis. There are several methods for acquiring genome-scale, phylogenetically-useful data from an organism that avoid sequencing the entire genome, thus reducing cost and effort, and enabling one to sequence many more individuals. In this dissertation we focus on one method in particular — RNA sequencing — and the concomitant use of assembled protein-coding transcripts in phylogeny reconstruction. Phylogenomic workflows involve tasks that are algorithmically and computationally demanding, in part due to the large amount of sequence data typically included in such analyses. This dissertation applies techniques from computer science to improve methodology and performance associated with phylogenomic workflow tasks such as sequence classification, transcript assembly, orthology determination, and phylogenetic analysis. While the majority of the methods developed in this dissertation can be applied to the analysis of diverse organismal groups, we primarily focus on the analysis of transcriptome data from Lepidoptera (moths and butterflies), generated as part of a collaboration known as “Leptree”