Reducing Energy in GPGPUs through Approximate Trivial Bypassing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
General-purpose computing using graphics processing units (GPGPUs) is an attractive option for acceleration of applications with massively data-parallel tasks. While performance of modern GPGPUs is increasing rapidly, the power consumption of these devices is becoming a major concern. In particular, execution units and register file are among the top three most power-hungry components in GPGPUs. In this work, we exploit trivial instructions to reduce power consumption in GPGPUs. Trivial instructions are those instructions that do not need computations, i.e., multiplication by one. We found that, during the course of a program's execution, a GPGPU executes many trivial instructions. Execution of these instructions wastes power unnecessarily. In this work, we propose trivial bypassing which skips execution of trivial instructions and avoids unnecessary allocation of resources for trivial instructions. By power gating execution units and skipping trivial computing, trivial bypassing reduces both static and dynamic power. Also, trivial bypassing reduces dynamic energy of register file by avoiding access to register file for source and/or destination operands of trivial instructions. While trivial bypassing reduces energy of GPGPUs, it has detrimental impact on performance as a power-gated execution unit requires several cycles to resume its normal operation. Conventional warp schedulers are oblivious to the status of execution units. We propose a new warp scheduler that prioritizes warps based on availability of execution units. We also propose a set of new power management techniques to reduce performance penalty of power gating, further. To increase energy saving of trivial bypassing, we also propose approximating operands of instructions. We offer a set of new techniques to approximate both integer and floating-point instructions and increase the pool of trivial instructions. Our evaluations using a diverse set of benchmarks reveal that our proposed techniques are able to reduce energy of execution units by 11.2% and dynamic energy of register file by 12.2% with minimal performance and quality degradation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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