Lectures on Parallel Computation

Parallel computation framework for optimizing trailer routes in bulk transportation

We consider a rich tanker trailer routing problem with stochastic transit times for chemicals and liquid bulk orders. A typical route of the tanker trailer comprises of sourcing a cleaned and prepped trailer from a pre-wash location, pickup and delivery of chemical orders, cleaning the tanker trailer at a post-wash location after order delivery and prepping for the next order. Unlike traditiona...

Parallel Functional Computation on STAR:DUST

STAR:DUST (‘St. Andrews RISC: Dataflow Using Sequential Threads’) is a processor design optimized for efficient execution of sequential threads while supporting plug-and-play construction of large multiprocessor systems. Besides satisfying the major RISC criteria (small instruction set, simple instruction format, load/store principle, pipelining), STAR:DUST employs a dataflow approach to commun...

On the parallel computation thesis

We develop a generic programming language for parallel algorithms, one that works for all data structures and control structures. We show that any parallel algorithm satisfying intuitively-appealing postulates can be modeled by a collection of cells, each of which is an abstract state machine, augmented with the ability to spawn new cells. The cells all run the same algorithm and communicate vi...

Lectures

Parallel Streaming Computation on Error-Prone Processors

Hardware fault rates are increasing due to decreasing transistor sizes in newer technology nodes. To ensure reliability, circuits need more redundancy and higher voltage margins, incurring high area/performance/energy overheads. Since some applications, such as multimedia processing and streaming applications, can tolerate some types of errors, approximate computing research has begun exploring...