Evaluation of a High-Level-Language Methodology for High-Performance Reconfigurable Computers
Bibliographic record
Abstract
High-performance reconfigurable computers (HPRCs) consisting of CPUs with application-specific FPGA accelerators traditionally use a low-level hardware-description language such as VHDL or Verilog to program the FP-GAs. The complexity of hardware design methodologies for FPGAs requires specialist engineering knowledge and presents a significant barrier to entry for scientific users with only a software background. Recently, a number of High-Level Languages (HLLs) for programming FPGAs have emerged that aim to lower this barrier and abstract away hardware-dependent details. This paper presents the results of a study on implementing hardware accelerators using the Mitrion-C HLL. The implementation of two floating-point scientific kernels: dense matrix-vector multiplication (DMVM) and the computation of spherical boundary conditions in molecular dynamics (SB) are described. We describe optimizations that are essential for taking advantage of both the features of the HLL and the underlying HPRC hardware and libraries. Scaling of the algorithms to multiple FPGAs is also investigated. With four FPGAs, 80 times speedup over an Itanium 2 CPU was achieved for the DMVM, while a 26 times speedup was achieved for SB.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".