Heterogeneous Habanero-C (H2C): A Portable Programming Model for Heterogeneous Processors
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
Heterogeneous architectures with their diverse architectural features impose significant programmability challenges. Existing programming systems involve non-trivial learning and are not productive, not portable, and are challenging to tune for performance. In this paper, we introduce Heterogeneous Habanero-C (H2C), which is an implementation of the Habanero execution model for modern heterogeneous (CPU + GPU) architectures. The H2C language provides high-level constructs to specify the computation, communication, and synchronization in a given application. H2C also implements novel constructs for task partitioning and locality. The H2C (source-to-source) compiler and runtime framework efficiently map these high-level constructs onto the underlying heterogeneous platform, which can include multiple CPU cores and multiple GPU devices, possibly from different vendors. Experimental evaluations of four applications show significant improvements in productivity, portability, and performance.
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 | 0.000 | 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 | 0.001 | 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