DistCL: A Framework for the Distributed Execution of OpenCL Kernels
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Bibliographic record
Abstract
GPUs are used to speed up many scientific computations, however, to use several networked GPUs concurrently, the programmer must explicitly partition work and transmit data between devices. We propose DistCL, a novel framework that distributes the execution of penCL kernels across a GPU cluster. DistCL makes multiple distributed compute devices appear to be a single compute device. DistCL abstracts and manages many of the challenges associated with distributing a kernel across multiple devices including: (1) partitioning work into smaller parts, (2) scheduling these parts across the network, (3) partitioning memory so that each part of memory is written to by at most one device, and (4) tracking and transferring these parts of memory. Converting an OpenCL application to DistCL is straightforward and requires little programmer effort. This makes it a powerful and valuable tool for exploring the distributed execution of OpenCL kernels. We compare DistCL to SnuCL, which also facilitates the distribution of OpenCL kernels. We also give some insights: distributed tasks favor more compute bound problems and favour large contiguous memory accesses. DistCL achieves a maximum speedup of 29.1 and average speedups of 7.3 when distributing kernels among 32 peers over an Infiniband cluster.
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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 it