Bounding memory access interferences on the Kalray MPPA3 compute cluster
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
The Kalray MPPA3 Coolidge many-core processor is one of the few off-the-shelf high-performance processors amenable to full-fledged static timing analysis. And yet, even on this processor, providing tight execution time upper bounds may prove difficult. In this paper, we consider the sub-problem of bounding the timing overhead due to memory access interferences inside one MPPA3 shared memory compute cluster. This includes interferences between computing cores and interferences between the instruction and data accesses of a given core. We start with a detailed analysis of the MPPA3 compute cluster, with emphasis on three key components: the Prefetch Buffer (PFB), which performs speculative instruction loads, the fixed-priority (FP) arbiter between instruction and data accesses of a core, whose behavior is highly dependent (in the worst case) on interferences from other cores, and the SAP (bursty Round Robin) arbiters guarding access to memory banks. We provide a full-fledged interference analysis covering both levels. This analysis is rooted in a novel modeling of memory access patterns, which describes their worst- case and best-case burstiness, a key factor influencing the MPPA3 arbitration. We evaluate our interference model on multiple applications, ranging from real-life avionics code specified in SCADE to linear algebra code. We also suggests methods for reducing execution time and improving analysis precision by means of code generation.
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 | 0.019 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.012 | 0.001 |
| Open science | 0.010 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| 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