SHADOW: Simultaneous Multi-Threading Architecture with Asymmetric Threads
Bibliographic record
Abstract
Many important applications exhibit shifting demands between instruction-level parallelism (ILP) and thread-level parallelism (TLP) due to irregular sparsity and unpredictable memory access patterns.Conventional CPUs optimize for one but fail to balance both, leading to underutilized execution resources and performance bottlenecks.Addressing this challenge requires an architecture that can seemlessly and efficiently adapt to workload variations.This paper presents SHADOW, the first asymmetric SMT core that dynamically balances ILP and TLP by executing out-of-order (OoO) and in-order (InO) threads simultaneously on the same core.SHADOW maximizes CPU utilization by leveraging deep ILP in the OoO thread and high TLP in lightweight InO threads.It is runtimeconfigurable, allowing applications to optimize the mix of OoO and InO execution.Evaluated on nine diverse benchmarks, SHADOW achieves up to 3.16× speedup and 1.33× average improvement over an OoO CPU, with just 1% area and power overhead.By dynamically adapting to workload characteristics, SHADOW outperforms conventional architectures, efficiently accelerating memory-bound workloads without compromising compute-bound performance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".