Code layout optimizations for transaction processing workloads
Bibliographic record
Abstract
Commercial applications such as databases and Web servers constitute the most important market segment for high-performance servers. Among these applications, on-line transaction processing (OLTP) workloads provide a challenging set of requirements for system designs since they often exhibit inefficient executions dominated by a large memory stall component. This behavior arises from large instruction and data footprints and high communication miss rates. A number of recent studies have characterized the behavior of commercial workloads and proposed architectural features to improve their performance. However, there has been little research on the impact of software and compiler-level optimizations for improving the behavior of such workloads. This paper provides a detailed study of profile-driven compiler optimizations to improve the code layout in commercial workloads with large instruction footprints. Our compiler algorithms are implemented in the context of Spike, an executable optimizer for the Alpha architecture. Our experiments use the Oracle commercial database engine running an OLTP workload, with results generated using both full system simulations and actual runs on Alpha multiprocessors. Our results show that code layout optimizations can provide a major improvement in the instruction cache behavior, providing a 55% to 65% reduction in the application misses for 64-128K caches. Our analysis shows that this improvement primarily arises from longer sequences of consecutively executed instructions and more reuse of cache lines before they are replaced. We also show that the majority of application instruction misses are caused by self-interference. However, code layout optimizations significantly reduce the amount of self-interference, thus elevating the relative importance of interference with operating system code. Finally, we show that better code layout can also provide substantial improvements in the behavior of other memory system components such as the instruction TLB and the unified second-level cache. The overall performance impact of our code layout optimizations is an improvement of 1.33 times in the execution time of our workload.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".