iCHAT: Inter-cache Hardware-Assistant Data Transfer for Heterogeneous Chip Multiprocessors
Bibliographic record
Abstract
Modern heterogeneous multiprocessors integrate CPU and GPU together to provide a boost to computational performance. Data sharing and communication between CPU and GPU has been a critical issue for the final speedup. With tighter integration of CPU and GPU, it has the advantage of sharing and moving data more efficiently in order to leverage the computational power that a GPU can provide. Initially, DMA or PCIe devices were used to transfer data between CPU and GPU with low efficiency and little flexibility. Recently a single address space and coherent cache hierarchies are being adopted in heterogeneous architectures to share data more efficiently. Thus it poses new challenge to understand the communication overheads in this new context and to improve communication efficiencies for these architectures. This paper proposes a novel approach called iCHAT (inter-Cache Hardware-Assistant data Transfer) to manage data transfer between the CPU cache and the GPU cache efficiently. The iCHAT technique proposed in this paper detects the communication patterns and eagerly evicts data from the owner's caches and prepares for the requestor's demand. We implement the iCHAT design in a simulator based on gem5 and an AMD in-house GPU simulator. Experimental results show that the communication related eviction traffic is reduced by an average of 40% and the total directory traffic is reduced by 8% on average. We implement a bounding experiment that provides a quantitative evaluation of inter CPU-GPU transfers and requests to communication data, which indicates that iCHAT could achieve on average 1.4x speedup for Rodinia benchmark suite and 1.2x speedup for AMD SDK APPs.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".