Implementation of decoders for symmetric low density parity check codes on parallel computation platforms using OpenCL
Bibliographic record
Abstract
OpenCL is a high-level language that allows mixed hardware/software systems to be specified and compiled to run on heterogeneous parallel computing platforms. The hardware parallelism can take the form of multi-core central processing units (CPUs), massively parallel graphics processing units (GPUs), and, most recently, field-programmable gate array (FPGA) fabrics. OpenCL compilers for CPUs and GPUs have been available for over 6 years, but only recently has compiler support been extended to include FPGAs. This paper investigates OpenCL designs for the computationally demanding standard iterative decoding algorithm for low-density parity check (LDPC) codes. The LDPC decoding algorithm offers several kinds of potentially exploitable parallelism. Our objective was to investigate the design trade-offs in OpenCL that will produce the best performance when compiled by the Altera Offline Compiler (AOC v15.1) for OpenCL-to-FPGA. Within a relatively short design time we were able to implement a decoder that achieved 5.12 Mbps with a length-1024 (3,6)-regular code.
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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 | 0.001 |
| 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 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".