A computer cluster combines interconnected computers, or nodes, into one system for computational work. Instead of running a large calculation on one machine, it can divide the work among nodes that process subtasks in parallel. section 7.3.1
A practical cluster workflow typically involves:
- Splitting a workload into smaller subtasks. section 7.3.1
- Running independent subtasks simultaneously across nodes. section 7.3.1
- Synchronizing processes, handling failures, and gathering final results. section 7.3.1
- Using an established framework rather than implementing distributed coordination from scratch. section 7.3.1
Parallel distance calculation
Calculating straight-line distances between pairs of points is a strong cluster example: each pair can be processed independently, with communication needed only to collect the results. By contrast, a calculation with frequent dependencies may be a poor candidate for parallelization. section 7.3.1
Cloud deployment can provide a cluster through Dask and a provider such as Google Cloud Platform, Amazon Web Services, or Microsoft Azure. Moving first to a single virtual machine may require no code changes but offers less computational power; distributing across a cluster requires setup and careful validation. section 7.3.2
