Design and Implementation of Burst Buffer Over-Subscription Scheme for HPC Storage Systems
Bibliographic record
Abstract
Burst Buffer is widely used in supercomputer centers to bridge the performance gap between computational power and the high-performance I/O systems. The primary role of Burst Buffer is to temporarily absorb the bursty I/O and reduce the heavy access on Parallel File System (PFS). However, the job resource manager on High-Performance Computer (HPC) systems prefers to use a dedicated Burst Buffer allocation approach, which eventually leads to the severely underutilized Burst Buffer resource. To improve the efficiency of using the expensive Burst Buffer resource, we analyze the I/O patterns on Burst Buffer in depth. We propose Burst Buffer over-subscription allocation method, which improves Burst Buffer utilization by allowing each job to access Burst Buffer only during its I/O phases so that the jobs can overlap each other. Furthermore, we develop a new I/O congestion-aware scheduler and a transparent data management system between Burst Buffer and PFS. Our approach also reduces the memory overhead and improves the data persistence of the data management system by adapting the persistent memory. With the proposed approach, not only the Burst Buffer utilization can be improved, but also HPC applications can achieve high I/O performance by exploiting the powerful Burst Buffer hardware capabilities. Experimental results show that BBOS can improve Burst Buffer utilization by up to 120% while more stable and higher checkpoint performance is guaranteed even under high I/O loads compared to other state-of-the-art schedulers. Besides, our approach can improve the hit ratio of restart requests by up to 96.4% and provides up to 210% higher restart throughput on Burst Buffer.
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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 | 0.002 |
| 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".