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Isolates are separate Dyalog APL processes that communicate by message passing. Use them for coarse-grained CPU-bound array work without shared memory, locks, or threading complexity.
Dyalog's ⎕PARALLEL directive allows for easy distribution of loop iterations across CPU cores. Learn when to use it for heavy array workloads and how to avoid race conditions.
Coarray Fortran lets program images share memory directly through square-bracket syntax, with explicit synchronization required between parallel reads and writes.
Coarray Fortran lets each program image access another image's memory directly. Learn the syntax, synchronization rules, and a correct halo‑exchange example.
Decide when to use Fortran coarrays instead of MPI for distributed-memory parallel code, with a minimal example and compiler validation steps.
Learn how to architect fault‑tolerant, asynchronous MATLAB code with parfeval, covering requirements, minimal design, data isolation, operational checks, failure handling, and when to re‑design. Includes example code and verification steps.
OpenCL's local memory can cut global bandwidth pressure dramatically—but only if you size tiles to fit hardware limits, avoid bank conflicts, and verify with profiling. A tiled matrix multiply shows the mechanics and the trade-offs.
Accelerate Monte Carlo π estimation in MATLAB by moving a large, independent loop to parfor. The post explains when parallelism pays off, shows a ready‑to‑run script, and covers random‑stream handling, memory, and verification steps.
Run independent APL workers in parallel with Dyalog isolates, avoiding shared-state bugs at the cost of copying data.
dyalog
matlab
opencl
apl