An experimental evaluation of Giraph and GraphCHI
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
We focus on the vertex-centric (VC) model introduced in Pregel, a Google system for distributed graph processing. In particular, we consider two popular implementations of the VC model: Apache Giraph and GraphChi. The first is a VC system for cluster computing, while the second is a VC system for a single PC. Apache Giraph became very popular after careful engineering by Facebook researchers in 2012 to scale the computation of PageRank to a trillion-edge graph of user interactions using 200 machines. On the other hand, GraphChi became popular, around the same time in 2012, as it made possible to perform intensive graph computations in a single PC, in just under 59 minutes, whereas the distributed systems were taking 400 minutes using a cluster of about 1,000 computers (as reported also by MIT Technology Review). Since then, new versions of Apache Giraph and GraphChi have been released, where new ideas and optimizations have been implemented. Therefore, it is time to validate again the claims made four years ago. In this work, we embark in this validation. We consider three cornerstone graph problems: computing PageRank, shortest-paths, and weakly-connected-components. Based on current experiments, we conclude that in the present, even for a moderate number of simple machines, Apache Giraph outperforms GraphChi for all the algorithms and datasets tested. This is in contrast to the claims of the GraphChi authors in 2012.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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