ALPHA-PIM: Analysis of Linear Algebraic Processing for High-Performance Graph Applications on a Real Processing-In-Memory System
Bibliographic record
Abstract
Processing large-scale graph datasets is computationally intensive and time-consuming. Processor-centric CPU and GPU architectures, commonly used for graph applications, often face bottlenecks caused by extensive data movement between the processor and memory units due to low data reuse. As a result, these applications are often memory-bound, limiting both performance and energy efficiency due to excessive data transfers. Processing-In-Memory (PIM) offers a promising approach to mitigate data movement bottlenecks by integrating computation directly within or near memory. Although several previous studies have introduced custom PIM proposals for graph processing, they do not leverage real-world PIM systems.This work aims to explore the capabilities and characteristics of common graph algorithms on a real-world PIM system to accelerate data-intensive graph workloads. To this end, we (1) implement representative graph algorithms on UPMEM’s general-purpose PIM architecture; (2) characterize their performance and identify key bottlenecks; (3) compare results against CPU and GPU baselines; and (4) derive insights to guide future PIM hardware design.Our study underscores the importance of selecting optimal data partitioning strategies across PIM cores to maximize performance. Additionally, we identify critical hardware limitations in current PIM architectures and emphasize the need for future enhancements across computation, memory, and communication subsystems. Key opportunities for improvement include increasing instruction-level parallelism, developing improved DMA engines with non-blocking capabilities, and enabling direct interconnection networks among PIM cores to reduce data transfer overheads.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".