T4: Compiling Sequential Code for Effective Speculative Parallelization in Hardware
Why this work is in the frame
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Bibliographic record
Abstract
Multicores are now ubiquitous, but programmers still write sequential code. Speculative parallelization is an enticing approach to parallelize code while retaining the ease of sequential programming, making parallelism pervasive. However, prior speculative parallelizing compilers and architectures achieved limited speedups due to high costs of recovering from misspeculation and hardware scalability bottlenecks. We present T4, a parallelizing compiler that successfully leverages recent hardware features for speculative execution, which present new opportunities and challenges for automatic parallelization. T4 transforms sequential programs into trees of tiny timestamped tasks. T4 introduces novel compiler techniques to expose parallelism aggressively across the entire program, breaking applications into tiny tasks of tens of instructions each. Task trees unfold their branches in parallel to enable high task-spawn throughput while exploiting selective aborts to recover from misspeculation cheaply. T4 exploits parallelism across function calls, loops, and loop nests; performs new transformations to reduce task spawn costs and avoid false sharing; and exploits data locality among fine-grain tasks. As a result, T4 scales several hard-to-parallelize SPECCPU2006 benchmarks to tens of cores, on which prior work attained little or no speedup.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it