Characterizing Large Dataset GPU Compute Workloads Targeting Systems with Die-Stacked Memory
Bibliographic record
Abstract
The increasing adoption of GPUs as mainstream computing devices, coupled with the imminent availability of large high-bandwidth caches based on die-stacked memory makes it important to analyze and understand modern GPU compute applications from the perspective of their memory access and data reuse characteristics. This paper presents detailed workload characterization studies on four GPU compute applications that process large data sets. The applications studied include tree traversal and search algorithms, a partial differential equation (PDE) solver, and a synthetic array processing application. Our studies indicate that while the memory footprint consumed by these applications can be very large, the effectiveness of several GB worth of cache may vary significantly across workloads. This suggests that provisioning cache resources in a die-stacked memory based system needs to be done very carefully, through detailed characterization of target workloads. An added benefit of our work was the discovery that accurate memory characterization data can lead to a significantly more optimized strategy for scheduling GPU threads by taking advantage of a workload's access characteristics. In particular, for the PDE solver, our analysis led to an optimization that achieved 30% measured gain in application performance. This paper also describes our analysis methodology for conducting these types of studies. The methodology is based on trace analysis, where the traces capture memory traffic and calls to the GPU compute API. For each application we highlight the characterization metrics and analysis techniques that were most useful in generating insights about their memory access patterns.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".