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Record W2005064465 · doi:10.1145/2749469.2750403

Quantitative comparison of hardware transactional memory for Blue Gene/Q, zEnterprise EC12, Intel Core, and POWER8

2015· article· en· W2005064465 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicDistributed systems and fault tolerance
Canadian institutionsIBM (Canada)
Fundersnot available
KeywordsTransactional memoryComputer scienceIBMSoftware transactional memoryScalabilityOperating systemOverhead (engineering)SoftwareMemory managementMulti-core processorParallel computingComputer architectureProgramming languageDatabase transaction

Abstract

fetched live from OpenAlex

Transactional Memory (TM) is a new programming paradigm for both simple concurrent programming and high concurrent performance. Hardware Transactional Memory (HTM) is hardware support for TM-based programming. It has lower overhead than software transactional memory (STM), which is a software-based implementation of TM. There are now four commercial systems, IBM Blue Gene/Q, IBM zEnterprise EC12, Intel Core, and IBM POWER8, offering HTM. Our work is the first to compare the performance of these four HTM systems. We measured the STAMP benchmarks, the most widely used TM benchmarks. We also evaluated the specific features of each HTM system. Our experimental results show that: (1) there is no single HTM system that is more scalable than the others in all of the benchmarks, (2) there are measurable performance differences among the HTM systems in some benchmarks, and (3) each HTM system has its own implementation characteristics that limit its scalability.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.918
Threshold uncertainty score0.408

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.091
GPT teacher head0.344
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations83
Published2015
Admission routes1
Has abstractyes

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