A Portable and High-Performance General Matrix-Multiply (GEMM) Library for GPUs and Single-Chip CPU/GPU Systems
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
OpenCL is a vendor neutral and portable interface for programming parallel compute devices such as GPUs. Tuning OpenCL implementations of important library functions such as dense general matrix multiply (GEMM) for a particular device is a difficult problem. Further, OpenCL kernels tuned for a particular architecture perform poorly on other architectures. We present a solution to the challenge of writing a portable and high-performance GEMM implementation. We designed and implemented RaijinCL, an OpenCL auto-tuning library for real and complex variants of GEMM that automatically generates tuned kernels for a given architecture. We comprehensively tested our library on a wide variety of architectures and show that the library is competitive with vendor libraries on all tested architectures. We also implemented an autotuner for hybrid CPU+GPU GEMM that takes advantage of both the CPU and GPU on singlechip CPU+GPU platforms such as Intel Ivy Bridge. We show that our solution can outperform CPU-only, GPU-only as well as simple CPU+GPU tuning strategies. In addition to performance results, we provide analysis of architectural limitations as well as OpenCL compiler and runtime issues discovered on various systems, along with guidance on avoiding some of these issues.
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 | 0.001 |
| Open science | 0.000 | 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