Porting a neuro-imaging application to a CPU-GPU cluster
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The ever increasing complexity of scientific applications has led to utilization of new HPC paradigms such as Graphical Processing Units (GPUs). However, modifying existing applications to enable them to be executed on GPU can be challenging. Furthermore, the considerable speedup achieved by execution of linear algebra operations on GPUs has added a huge heterogeneity to HPC clusters. In this work, we enabled NPAIRS, a neuro-imaging application, to be executed on GPU with slight modifications to its original code. This important feature of our implementation enables current users of NPAIRS, i.e. non-expert bio-medical scientists, to get benefit from GPU without having to apply fundamental changes to their existing application. As the second part of our research, we investigated the efficiency of several scheduling algorithms for a heterogeneous cluster that contains GPU nodes. Experimental results show that we achieved 7× speedup for NPAIRS. Moreover, although scheduling does not play an important role when there is no GPU node in the cluster, it can highly improve the makespan for a CPU-GPU cluster. We compared our scheduling results with Torque and MCT, two of the most commonly used schedulers in current HPC platforms. Our results show that the Sufferage scheduling can improve the makespan of Torque and MCT by 47% and 4% respectively.
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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 | 0.001 | 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