Balancing Graph Processing Workloads Using Work Stealing on Heterogeneous CPU-FPGA Systems
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Bibliographic record
Abstract
We propose, implement and evaluate a work stealing based scheduler, called HWS, for graph processing on heterogeneous CPU-FPGA systems that tightly couple the CPU and the FPGA to share system memory. HWS addresses unique concerns that arise with work stealing in the context of our target system. Our evaluation is conducted on the Intel Heterogeneous Architecture Research Platform (HARPv2), using three key processing kernels and seven real-world graphs. We show that HWS effectively balances workloads. Further, the use of HWS results in better graph processing performance compared to static scheduling and a representative of existing adaptive partitioning techniques, called HAP. Improvements vary by graph processing application, input graph and number of threads, and can be up to 100% over static scheduling, and up to 17% over HAP. We also compare to an oracle chunk self-scheduler, in which the best chunk size is known a priori for each number of threads and each input graph. HWS performs no worse than 1-3% in most cases. Finally, our graph processing throughput scales well with increasing threads. These results collectively demonstrate the effectiveness of work stealing for graph processing on our heterogeneous target platform.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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