BigKernel -- High Performance CPU-GPU Communication Pipelining for Big Data-Style Applications
Why this work is in the frame
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Bibliographic record
Abstract
GPUs offer an order of magnitude higher compute power and memory bandwidth than CPUs. GPUs therefore might appear to be well suited to accelerate computations that operate on voluminous data sets in independent ways, e.g., for transformations, filtering, aggregation, partitioning or other "Big Data" style processing. Yet experience indicates that it is difficult, and often error-prone, to write GPGPU programs which efficiently process data that does not fit in GPU memory, partly because of the intricacies of GPU hardware architecture and programming models, and partly because of the limited bandwidth available between GPUs and CPUs. In this paper, we propose Big Kernel, a scheme that provides pseudo-virtual memory to GPU applications and is implemented using a 4-stage pipeline with automated prefetching to (i) optimize CPU-GPU communication and (ii) optimize GPU memory accesses. Big Kernel simplifies the programming model by allowing programmers to write kernels using arbitrarily large data structures that can be partitioned into segments where each segment is operated on independently, these kernels are transformed into Big Kernel using straight-forward compiler transformations. Our evaluation on six data-intensive benchmarks shows that Big Kernel achieves an average speedup of 1.7 over state-of-the-art double-buffering techniques and an average speedup of 3.0 over corresponding multi-threaded CPU implementations.
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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 | 0.002 | 0.001 |
| 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