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
KBLAS is an open-source, high-performance library that provides optimized kernels for a subset of Level 2 BLAS functionalities on CUDA-enabled GPUs. Since performance of dense matrix-vector multiplication is hindered by the overhead of memory accesses, a double-buffering optimization technique is employed to overlap data motion with computation. After identifying a proper set of tuning parameters, KBLAS efficiently runs on various GPU architectures while avoiding code rewriting and retaining compliance with the standard BLAS API. Another optimization technique allows ensuring coalesced memory access when dealing with submatrices, especially for high-level dense linear algebra algorithms. All KBLAS kernels have been leveraged to a multi-GPU environment, which requires the introduction of new APIs. Considering general matrices, KBLAS is very competitive with existing state-of-the-art kernels and provides a smoother performance across a wide range of matrix dimensions. Considering symmetric and Hermitian matrices, the KBLAS performance outperforms existing state-of-the-art implementations on all matrix sizes and achieves asymptotically up to 50% and 60% speedup against the best competitor on single GPU and multi-GPUs systems, respectively. Performance results also validate our performance model. A subset of KBLAS high-performance kernels have been integrated into NVIDIA's standard BLAS implementation (cuBLAS) for larger dissemination, starting from version 6.0.
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 | 0.001 |
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 it