Research & LabsAcademic collection

Parallel Computing Lab

Coursework exploring sequential, threaded and multiprocess computation.

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Discipline
Research & Labs
Build
Academic collection
Core stack
Python / Multiprocessing
Write-up
Project notes
The challenge

The problem behind the build

Parallel work needs explicit partitioning and a fair comparison with a sequential baseline.

The approach

How the pieces fit together

The exercises divide workloads among workers and retain separate implementations and plotting scripts for comparison.

Capabilities

Inside the implementation

Sequential and multiprocess counting examples

Workload partitioning and worker aggregation

Lab exercises and comparison plots

Technology

The stack behind this project

PythonMultiprocessingParallel Computing

Project scope

Academic exercises; no performance multiplier is claimed without a reproducible benchmark.

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