An overview of Altera SDK for OpenCL: A user perspective
Bibliographic record
Abstract
In recent years there has been a great interest in High Level Synthesis (HLS) CAD tools to raise the level of design abstraction, reduce design time, rapidly explore the design space and fully exploit the multi-million gate heterogeneous hardware platforms provided by dramatic improvements in integrated circuits. Open Computing Language (OpenCL) is a well-known standard for heterogeneous computing. The Altera SDK for OpenCL is used to convert OpenCL code to kernels that can be run on an FPGA accelerator card. It is a recently introduced HLS CAD tool that allows for the potential to convert existing, or create new C/C++ programs that utilize dedicated hardware to execute specific applications much faster and more efficient than current computer systems, whether single core or multi-core. This can all be done without the knowledge of FPGAs, VHDL, or Verilog as the SDK converts the OpenCL files into Verilog models that are then compiled into FPGA hardware. This paper presents a user-centric overview of Altera SDK for OpenCL. As a first step to achieve the best speedup, the candidate algorithm for acceleration must be analyzed to check if it is inherently parallelizable. The key features such as designing appropriate OpenCL kernels and host program, their compilation, execution and testing are summarized. A working example for accelerating a simple matrix multiplication algorithm is described. Our motivation is to provide the novice users with a useful tutorial that will enable them to quickly become proficient in using this important HLS CAD tool. To our knowledge, such a user-centric tutorial has not been presented so far in the literature.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.045 | 0.037 |
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".