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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".