Designing APU Oriented Scientific Computing Applications in OpenCL
Bibliographic record
Abstract
The future of high performance computing is moving towards exa-scale computing. Graphical Processing Units (GPUs) have demonstrated their capabilities beyond graphics rendering or general purpose computing and are well suited for data intensive applications. However, the communication bottleneck for data transfer between the GPU and CPU has led to the design of AMD's Accelerated Processing Unit (APU) which combines the CPU and GPU on a single chip. This new architecture poses new challenges: algorithms must be redesigned to take advantage of this architecture and programming models differ between vendors, hindering the portability of algorithms across heterogeneous platforms. Recently, OpenCL has been regarded as the standard programming model for heterogeneous platforms. With the future of general purpose computing moving towards APUs, in this paper, we study the design and implementation of two problems: 0-1 knapsack and Gaussian Elimination in OpenCL. This pair of algorithms showcases similar synchronization behaviors, enabling a more direct comparison. We discuss the design and performance of these algorithms using OpenCL.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".