Accelerating shared virtual memory via general-purpose network interface support
Bibliographic record
Abstract
Clusters of symmetric multiprocessors (SMPs) are important platforms for high-performance computing. With the success of hardware cache-coherent distributed shared memory (DSM), a lot of effort has also been made to support the coherent shared-address-space programming model in software on clusters. Much research has been done in fast communication on clusters and in protocols for supporting software shared memory across them. However, the performance of software virtual memory (SVM) is still far from that achieved on hardware DSM systems. The goal of this paper is to improve the performance of SVM on system area network clusters by considering communication and protocol layer interactions. We first examine what are the important communication system bottlenecks that stand in the way of improving parallel performance of SVM clusters; in particular, which parameters of the communication architecture are most important to improve further relative to processor speed, which ones are already adequate on modern systems for most applications, and how will this change with technology in the future. We find that the most important communication subsystem cost to improve is the overhead of generating and delivery interrupts for asynchronous protocol processing. Then we proceed to show, that by providing simple and general support for asynchronous message handling in a commodity network interface (NI) and by altering SVM protocols appropriately, protocol activity can be decoupled from asynchronous message handling, and the need for interrupts or polling can be eliminated. The NI mechanisms needed are generic, not SVM-dependent. We prototype the mechanisms and such asynchronous home-basedLRCprotocol, calledGeNIMA(GEneral-purpose Network Interface support for shared Memory Abstractions), on a cluster of SMPs with a programmable NI. We find that the performance improvements are substantial, bringing performance on a small-scale SMP cluster much closer to that of hardware-coherent shared memory for many applications, and we show the value of each of the mechanisms in different applications.
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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 | 0.002 |
| 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 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".