This paper explores the scalability of the Stream Processor architecture along the instruction-, data-, and thread-level parallelism dimensions. We develop detailed VLSI-cost and ...
Bias/variance analysis is a useful tool for investigating the performance of machine learning algorithms. Conventional analysis decomposes loss into errors due to aspects of the le...
A challenging issue facing Grid communities is that while Grids can provide access to many heterogeneous resources, the resources to which access is provided often do not match th...
Ian T. Foster, Timothy Freeman, Katarzyna Keahey, ...
We propose a new semi-supervised model selection method that is derived by applying the structural risk minimization principle to a recent semi-supervised generalization error bou...
A master/worker paradigm for executing large-scale parallel discrete event simulation programs over networkenabled computational resources is proposed and evaluated. In contrast t...