The impact of negative acknowledgments in shared memory scientific applications
Bibliographic record
Abstract
Negative acknowledgments (NACKs) and subsequent retries, used to resolve races and to enforce a total order among shared memory accesses in distributed shared memory (DSM) multiprocessors, not only introduce extra network traffic and contention, but also increase node controller occupancy, especially at the home. We present possible protocol optimizations to minimize these retries and offer a thorough study of the performance effects of these messages on six scalable scientific applications running on 64-node systems and larger. To eliminate NACKs, we present a mechanism to queue pending requests at the main memory of the home node and augment it with a novel technique of combining pending read requests, thereby accelerating the parallel execution for 64 nodes by as much as 41 percent (a speedup of 1.41) compared to a modified version of the SGI Origin 2000 protocol. We further design and evaluate a protocol by combining this mechanism with a technique that we call write string forwarding, used in the AlphaServer GS320 and Piranha systems. We find that without careful design considerations, especially regarding atomic read-modify-write operations, this aggressive write forwarding can hurt performance. We identify and evaluate the necessary micro-architectural support to solve this problem. We compare the performance of these novel NACK-free protocols with a base bitvector protocol, a modified version of the SGI Origin 2000 protocol, and a NACK-free protocol that uses dirty sharing and write string forwarding as in the Piranha system. To understand the effects of network speed and topology the evaluation is carried out on three network configurations.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".