Virtual Application Appliances in Practice: Basic Mechanisms and Overheads
Bibliographic record
Abstract
Virtual application appliances (VAA) (i.e., prebuilt virtual machines (VM) for specific scientific applications) are useful mechanisms to deal with the packaging of complex software systems and heterogeneous software environments (e.g., library version conflicts on different clusters and clouds). As an experience paper, we discuss some basic techniques for creating VAAs (e.g., virtual disk repositories (VDR)) and scripting their execution. As an evaluation paper, we quantify some of the key overheads, including the cost of staging data into/out of the VAA and the costs of VM migration. Subsequently, we introduce and evaluate a new Copy Over Shared Memory (CSM) mechanism to reduce the stage in/out overheads of data using secure, shared-memory regions between the host and guest machines. Our empirical evaluation shows that VAAs achieve nearnative, end-to-end performance in widely used bioinformatics applications that we tested (i.e., GROMACS, GAFolder, HMMer). We focus on data movement, VM boot up, shutdown and migration overheads of VAAs and find that they are negligible with respect to total run-times.
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 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 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".