CableS : thread control and memory management extensions for shared virtual memory clusters
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
Clusters of high-end workstations and PCs are currently used in many application domains to perform large-scale computations or as scalable servers for I/O bound tasks. Although clusters have many advantages, their applicability in emerging areas of applications has been limited. One of the main reasons for this is the fact that clusters do not provide a single system image and thus are hard to program. In this work we address this problem by providing a single-cluster image with respect to thread and memory management. We implement our system, CableS (Cluster enabled threads), on a 32-processor cluster interconnected with a low-latency, high-bandwidth system area network and conduct an early exploration of the costs involved in providing the extra functionality. We demonstrate the versatility :of Cables with a wide range of applications and show that clusters can be used to support applications that have been written for more expensive tightly-coupled systems, With very little effort on the programmer side: (a) We run legacy pthreads applications without any major modifications. (b) We use a public domain OpenMP compiler (OdinMP) to translate OpenMP programs to pthreads and execute them on our system, with no or few modifications to the translated pthreads source code. (c) We provide an implementation of the M4 macros for our pthreads system and run the SPLASH-2 applications. We also show that the overhead introduced by the extra functionality of CableS affects the parallel section of applications that have been tuned for the shared memory abstraction only in cases where the data placement is affected by operating system (WindowsNT) limitations in virtual memory mappings granularity.
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