Finite-difference time-domain on the cell/B.E. processor
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
Finite-Difference Time-Domain (FDTD) is a kernel used to solve problems in electromagnetics applications such as microwave tomography. It is a data-intensive and computation-intensive problem. However, its computation scheme indicates that an architecture with SIMD support has the potential to bring performance improvement over traditional architectures without SIMD support. The Cell Broadband Engine (Cell/B.E.) processor is an implementation of a heterogeneous multicore architecture. It consists of one conventional microprocessor, PowerPC Processor Element (PPE), and eight SIMD co-processor elements, Synergistic Processor Elements (SPEs). One unique feature of an SPE is that it has 128-entry 128-bit uniform registers which support SIMD. Therefore, FDTD may be mapped well on Cell/B.E. processor. However, each SPE can directly access only 256KB local store (LS) both for instructions and data. The size ofLS is much less than what is needed for an accurate simulation of FDTD which requires large number of fine-grained Yee cells. In this paper, we design the algorithm on Cell/B.E. by efficiently using the asynchronous DMA (direct memory access) mechanism available on an SPE transferring data between its LS and the main memory via the high bandwidth bus on-chip EIB (Element Interconnect Bus). The new algorithm was run on an IBM Blade QS20 blades running at 3.2GHz. For a computation domain of 600 x 600 Yee cells, we achieve an overall speedup of 14.14 over AMD Athlon and 7.05 over AMD Opteron at the processor level.
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 | 0.000 |
| 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