How Well do CPU, GPU and Hybrid Graph Processing Frameworks Perform?
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 importance of high-performance graph processing to solve big data problems targeting high-impact applications is greater than ever before. Recent graph processing frameworks target different hardware platforms (e.g., shared memory systems, accelerators such as GPUs, and distributed systems) and differ with respect to the programming model they adopt (e.g., based on linear algebra formulations of graph algorithms or enabling direct access to the graph structure). To better understand the impact of these choices, this paper, presents a comparative study of five state-of-the-art graph processing frameworks: two CPU-only frameworks - GraphMat and Galois, two GPU-based frameworks - Nvgraph and Gunrock; and Totem, a hybrid (CPU+GPU) framework. We use three popular graph algorithms (PageRank, Single Source Shortest Path, and Breadth-First Search), and massive scale graphs with up to billions of edges. Our evaluation focuses on three performance metrics: (i) execution time, (ii) scalability and (iii) energy consumption.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 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