Linearly Compressed Pages: A Main Memory Compression Framework with Low Complexity and Low Latency
Bibliographic record
Abstract
Data compression is a promising technique to address the increasing main memory capacity demand in future systems. Unfortunately, directly applying previously proposed compression algorithms to main memory requires the memory controller to perform non-trivial computations to locate a cache line within the compressed main memory. These additional computations lead to significant increase in access latency, which can degrade system performance. Solutions proposed by prior work to address this performance degradation problem are either costly or energy ineffi- cient. In this paper, we propose a new main memory compression framework that neither incurs the latency penalty nor requires costly or power-inefficient hardware. The key idea behind our proposal is that if all the cache lines within a page are compressed to the same size, then the location of a cache line within a compressed page is simply the product of the index of the cache line within the page and the size of a compressed cache line. We call a page compressed in such a manner a Linearly Compressed Page (LCP). LCP greatly reduces the amount of computation required to locate a cache line within the compressed page, while keeping the hardware implementation of the proposed main memory compression framework simple. We adapt two previously proposed compression algorithms, Frequent Pattern Compression and Base-DeltaImmediate compression, to fit the requirements of LCP. Evaluations using benchmarks from SPEC CPU 2006 and five server benchmarks show that our approach can significantly increase the effective memory capacity (69% on average). In addition to the capacity gains, we evaluate the benefit of transferring consecutive compressed cache lines between the memory controller and main memory. Our new mechanism considerably reduces the memory bandwidth requirements of most of the evaluated benchmarks (46%/48% for CPU/GPU on average), and improves overall performance (6.1%/13.9%/10.7% for single-/two-/four-core CPU workloads on average) compared to a baseline system that does not employ main memory compression.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".