question

active

question:are-the-operations-that-create-and-control-parallelism-best-left-in-the-programmer-s-hands-or-should-they-be-turned-over-to-the-compiler

Are the operations that create and control parallelism best left in the programmer’s hands, or should they be turned over to the compiler?

Central design question underlying the comparison with functional approaches.

Source paper

extracted_from

Artificial Intelligence and Linda in Context

(1989) · Carriero, Nicholas · Gelernter, David

Related by similarity (8)

cosine ≥ 0.65 · no typed edge

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

Achieving automatic parallelization comparable to programmer-controlled granularity is a difficult problem.claim0.779
Scepticism about compilers fully automating granularity decisions.
Object-oriented programming is powerful and attractive, but irrelevant to parallelism.claim0.774
Claim that OOP per se does not solve any parallel programming problems.
Parallel programming needn't be terribly difficult, but 'thinking in simultaneities' as in message-passing is calculated to make it difficult.claim0.761
Asserts that Linda's uncoupled style reduces cognitive load.
Pure functional languages cannot be the whole story for parallel programming because they fail to provide needed expressivity.claim0.758
Argument that recursion equations are inappropriate for many important parallel programs.
Explicitly parallel programs are not inherently harder to design or understand than functional specifications.claim0.754
C-Linda DNA comparison is comparable in length and clarity to Crystal; pragmatic runtime granularity control outweighs compiler optimization ideals.
Shouldn’t we be tempted to recode these programs in functional languages, flip the autopilot switch, lean back, have a beer, and leave the parallelizing to them?question0.746
Rhetorical question about trusting compilers with parallelization.
Much of the usefulness of computation lies in data reduction—removing the haystack to leave the needleclaim0.743
Normal forms are exponentially large; computing them makes explicit only necessary information while discarding most input data, explaining apparent information increase.
Language models prefer reusing generic arithmetic mechanisms over learning task-specific modular computations even when task-specific geometry existsclaim0.741
Broader interpretive claim about LM learning bias inferred from the findings