EDGE: Event-Driven GPU Execution
Bibliographic record
Abstract
GPUs are known to benefit structured applications with ample parallelism, such as deep learning in a datacenter. Recently, GPUs have shown promise for irregular streaming network tasks. However, the GPU's co-processor dependence on a CPU for task management, inefficiencies with fine-grained tasks, and limited multiprogramming capabilities introduce challenges with efficiently supporting latency-sensitive streaming tasks. This paper proposes an event-driven GPU execution model, EDGE, that enables non-CPU devices to directly launch preconfigured tasks on a GPU without CPU interaction. Along with freeing up the CPU to work on other tasks, we estimate that EDGE can reduce the kernel launch latency by 4.4xcompared to the baseline CPU-launched approach. This paper also proposes a warp-level preemption mechanism to further reduce the end-to-end latency of fine-grained tasks in a shared GPU environment. We evaluate multiple optimizations that reduce the average warp preemption latency by 35.9x over waiting for a preempted warp to naturally flush the pipeline. When compared to waiting for the first available resources, we find that warp-level preemption reduces the average and tail warp scheduling latencies by 2.6x and 2.9x, respectively, and improves the average normalized turnaround time by 1.4x.
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.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".