Bibliographic record
Abstract
Programming models assist developers in creating high performance computing systems by forming a higher level abstraction of the target platform. OpenCL has emerged as a standard programming model for heterogeneous systems and there has been recent activity combining OpenCL and FPGAs. This work introduces memory infrastructure for FPGAs and is designed for OpenCL style computation, complementing previous work. An Aggregating Memory Controller is implemented in hardware and aims to maximize bandwidth to external, large, high-latency, high-bandwidth memories by finding the minimal number of external memory burst requests from a vector of requests. A template processing array with soft-processor and hand-coded hardware elements was also designed to drive the memory controller. The Aggregating Memory Controller is described in terms of operation and future scalability and the created processing array is described as a flexible structure that can support many types of processing solutions. A hardware prototype of the memory controller and processing array was implemented on a Virtex-5 LX110T FPGA. Two micro-benchmarks were run on both the soft-processor elements and the hand-coded hardware cores to exercise the memory controller. Results for effective memory bandwidth within the system show that the high-latency can be hidden using the Aggregating Memory Controller by increasing the number of threads within the processing array.
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.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.148 | 0.040 |
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".