A Cache-Coherent Heterogeneous Architecture for Low Latency Real Time Applications
Bibliographic record
Abstract
This paper proposes a generic hardware architecture for runtime acceleration of heterogeneous high performance computing (HPC) clusters. This runtime accelerator performs real time resource allocation and management of HPC systems with low latency on multiple time scales. One of the target applications is to perform the signal processing in wireless communication systems such as LTE and 5G over the cloud. A core part of this work is to develop and characterize algorithms that can distribute workloads to server blades in a balanced manner with the aim of maximizing processor utilization in computing clusters. Resources are also managed to guarantee bandwidth for data transfer between computing nodes and reserved cache memories to enable deterministic task execution. This paper shows how a workload distributed among several server blades can be scheduled at a finer time scale than what a normal software implementation would allow, in order to minimize the makespan required to complete execution of sets of tasks. A case study is conducted on the implementation of a resource allocator for the proposed platform. A 760-time acceleration factor of the resource allocation process has been achieved compared to a pure software implementation, while enabling data transfers at the nanosecond scale. It stands as a proof of concept that confirms the viability of CPU-FPGA platforms for wireless standards virtualization.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".