Portable software development for multi-core processors, many-core accelerators, and heterogeneous architectures
Bibliographic record
Abstract
New processor architectures, including many-core accelerators like GPUs, multi-core CPUs, and heterogeneous architectures like the Cell BE, provide many opportunities for improved performance. However, programming these architectures productively in a performant and portable way is challenging. We have developed a software development platform that uses a common SPMD parallel programming model for all these processor architectures. The RapidMind platform allows developers to easily create single-source, conceptually single-threaded programs with an existing, standard C++ compiler that can target all the processing resources in such architectures. When compared to tuned baseline code using the best optimizing C++ compilers available, RapidMind-enabled code can demonstrate speedups of over an order of magnitude on x86 dual-processor quad-core systems (more than the number of cores, due to the enhanced data locality of the RapidMind programming model) and two orders of magnitude on accelerators. In this talk, I will discuss the performance strategy used by the RapidMind platform, which is based on the observation that only two things really matter for performance: parallelism and data locality. A developer should be provided with mechanisms for direct and convenient expressions of these crucial facets of an implementation. At the same time, to enhance portability and productivity, a programming system should avoid over-specification of details that can be optimized by the system itself (in a portable way), and to minimize debugging should emphasize correct-by-construction parallel programming patterns. Finally, resource limits and performance cliffs inhibit portability, but by allowing the specification of parameterized code and by using auto-tuning, these issues can be addressed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".