Strategizing multi-resource processing
In the early days of strategizing multi-resource processing, large-scale algorithms were executed on powerful machines called supercomputers. These monolithic machines had a shared memory space, enabling quick communication between different processors and allowing them to access common variables through the same memory. As the demand for running large-scale algorithms grew, supercomputers transformed into Distributed Shared Memory (DSM) systems, where each processing node owned a segment of the physical memory. Subsequently, clusters emerged, constituting loosely connected systems that depend on message passing between processing nodes.
Effectively running large-scale algorithms requires multiple execution engines operating in parallel to tackle intricate challenges. Three primary strategies can be utilized to achieve this:
- Look within: Exploit the existing resources on a computer by using the hundreds of cores available on a...
