AWhat changed
The 2008 CUDA article explains a GPU programming model in which independently schedulable thread blocks combine local cooperation, shared memory and barriers, allowing a fixed problem decomposition to run across different processor counts.
Scientific scope
The 2008 CUDA article explains a GPU programming model in which independently schedulable thread blocks combine local cooperation, shared memory and barriers, allowing a fixed problem decomposition to run across different processor counts. The assessed target is the CUDA model documented in the 2008 article, following the stated 2007 release, rather than the entire later CUDA ecosystem or the invention of GPU computing. Blocks must execute correctly in arbitrary order without cross-block barrier dependence; local barriers and per-block shared memory enable cooperation. Global atomics are a limited coordination mechanism, not a general grid synchronization guarantee. Host/device data transfers, memory access, divergence and occupancy affect performance. The article's 512-thread limit, no kernel recursion or kernel-created grids and Tesla hardware details are historical, not current API specifications. Simple vector, CSR and reduction examples are illustrations, not independently tested production code; power-of-two reduction layout and floating-point order require care. Source-reported speedups and classroom experience do not establish universal speed or usability. Prior Brook/SPMD/parallel systems and separate CPU translation are inherited/contextual work. No GPU or benchmark replay was performed.
Evidence
Source-linked, scoped account of the original work. The published scope states the scientific assumptions and remaining verification limits. WRS has not independently certified the full proof, experimental result or system benchmark.
Contributors
- John Nickolls · SCIENCE: Original source-listed scientific author of this scoped work; byline does not determine effort fractions.
- Ian Buck · SCIENCE: Original source-listed scientific author of this scoped work; byline does not determine effort fractions.
- Michael Garland · SCIENCE: Original source-listed scientific author of this scoped work; byline does not determine effort fractions.
- Kevin Skadron · SCIENCE: Original source-listed scientific author of this scoped work; byline does not determine effort fractions.
- Nickolls, Buck, Garland and Skadron: Scalable Parallel Programming with CUDA (ACM Queue 6(2),40-53,2008), original article
- Kevin Skadron publication bibliography: exact CUDA article authors, journal, pages and DOI
Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.
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