DMM-GAPBS: Adapting the GAP Benchmark Suite to a Distributed Memory Model
Bibliographic record
Abstract
Due to the ability of graphs to model diverse real-world scenarios such as social networks, roads, or biological networks, effective graph processing techniques are of critical importance to a wide array of fields. As a consequence of the growth of data volumes, some graphs have already outgrown the memory capacities of single servers. In such cases, it is desirable to partition and keep the entire graph in a distributed memory space into order to bring the resources of a computing cluster to bear on the problem. This approach introduces a number of challenges, such as communication bottlenecks and low hardware utilization. However, it is difficult to effectively measure the impact of innovations addressing these challenges due to a lack of standardization in the domain of distributed graph processing. This research study was inspired by, and builds off of, the widely-used GAP Benchmark Suite (GAPBS), which was developed to provide an effective baseline and consistent set of evaluation methodologies for shared memory multiprocessor graph processing systems. We design and develop a new benchmark suite called DMM-GAPBS, a distributed-memory-model GAPBS. We adapt the GAPBS graph building infrastructure and algorithms, but utilize OpenSHMEM to enable a distributed memory environment, in the hope of providing a modular, extensible baseline for the distributed graph processing community. In order to showcase our design and implementation for processing graphs that cannot fit within a single server, we present the results of executing the DMM-GAPBS benchmark kernels on two large synthetic graphs distributed across sixteen nodes of an enterprise class system.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".