Paper Citation Record · LEDGER

Accelerating Latency-Critical Applications with AI-Powered Semi-Automatic Fine-Grained Parallelization on SMT Processors

As of 8 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2509.00883.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2509.00883 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T13:12:44.208428Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

22 of 22 outbound references displayed

verified exact6
verified fuzzy8
unresolved4
parse uncertain0
malformed identifier1
metadata mismatch3

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 82d19236-2914-4bbf-af68-12b118ddfbb6 · outbound

This paper cites Simultaneous multithreading: maximizing on -chip parallelism,.

Accelerating Latency-Critical Applications with AI-Powered Semi-Automatic Fine-Grained Parallelization on SMT Processors Simultaneous multithreading: maximizing on -chip parallelism,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:12:44.904090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 151fff03-e72c-435b-9ca8-194e2080f001 · outbound

This paper cites Efficiency of thread -level speculation in SMT and CMP architectures - performance, power and thermal perspective,.

Accelerating Latency-Critical Applications with AI-Powered Semi-Automatic Fine-Grained Parallelization on SMT Processors Efficiency of thread -level speculation in SMT and CMP architectures - performance, power and thermal perspective,

Reference 2

Resolution: metadata mismatch; raw_fallback, observed 2026-08-05T13:12:44.810601Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 32607f83-781c-4157-9e0b-ba453b4cd694 · outbound

This paper cites Speculative precomputation: long -range prefetching of delinquent loads,.

Accelerating Latency-Critical Applications with AI-Powered Semi-Automatic Fine-Grained Parallelization on SMT Processors Speculative precomputation: long -range prefetching of delinquent loads,

Reference 3

Resolution: metadata mismatch; raw_fallback, observed 2026-08-05T13:12:44.740365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 73152f67-0fcf-4f28-a6fa-d43aaf7a2e09 · outbound

This paper cites Exploring Fine-grained Task Par allelism o n Simultaneous Multithreading Cores,.

Accelerating Latency-Critical Applications with AI-Powered Semi-Automatic Fine-Grained Parallelization on SMT Processors Exploring Fine-grained Task Par allelism o n Simultaneous Multithreading Cores,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:12:44.893645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6619f63c-4d81-49f9-bec9-d265ce26059b · outbound

This paper cites OMPar: Automatic Parallelization with AI-Driven Source-to-Source Compilation.

Accelerating Latency-Critical Applications with AI-Powered Semi-Automatic Fine-Grained Parallelization on SMT Processors OMPar: Automatic Parallelization with AI-Driven Source-to-Source Compilation

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-08-05T13:12:44.666640Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 139daa16-8c91-4f26-93f0-318c8f73429c · outbound

This paper cites [Online].

Accelerating Latency-Critical Applications with AI-Powered Semi-Automatic Fine-Grained Parallelization on SMT Processors [Online]

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:12:44.883820Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 69c9c796-89ce-492c-87ec-3f39b97b63da · outbound

This paper cites Polly - Performing Polyhedral Optimization s o n a Low-Level Intermediate Representation.

Accelerating Latency-Critical Applications with AI-Powered Semi-Automatic Fine-Grained Parallelization on SMT Processors Polly - Performing Polyhedral Optimization s o n a Low-Level Intermediate Representation

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:12:44.874586Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2b54956f-f21e-4fb6-8a67-ec2132fc1299 · outbound

This paper cites The Polyhedral Model Is More Widely Applicable Than You Think.

Accelerating Latency-Critical Applications with AI-Powered Semi-Automatic Fine-Grained Parallelization on SMT Processors The Polyhedral Model Is More Widely Applicable Than You Think

Reference 8

Resolution: malformed identifier; no resolver link, observed 2026-08-05T13:12:44.161212Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7fe78138-da6b-45af-93fc-fcd2f2683bf4 · outbound

This paper cites Automatic construct selection and variable classification in OpenMP,.

Accelerating Latency-Critical Applications with AI-Powered Semi-Automatic Fine-Grained Parallelization on SMT Processors Automatic construct selection and variable classification in OpenMP,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:12:44.864645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4b8ff464-4a33-4518-a078-9375560095f2 · outbound

This paper cites Adapting the polyhedral model as a f r am ewo rk for efficient speculative parallelization,.

Accelerating Latency-Critical Applications with AI-Powered Semi-Automatic Fine-Grained Parallelization on SMT Processors Adapting the polyhedral model as a f r am ewo rk for efficient speculative parallelization,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:12:44.853335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 273c20d4-8ff9-45ea-9908-0a55bcc585ba · outbound

This paper cites Par4All: From Convex Array Regions to Heterogeneous Computing.

Accelerating Latency-Critical Applications with AI-Powered Semi-Automatic Fine-Grained Parallelization on SMT Processors Par4All: From Convex Array Regions to Heterogeneous Computing

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:12:44.843348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2eec40a7-69ac-4e40-8b3d-35299885ab43 · outbound

This paper cites Automatic Transformations for Communication-Minimized Parallelization and Locality Optimization in the Polyhedral Model.

Accelerating Latency-Critical Applications with AI-Powered Semi-Automatic Fine-Grained Parallelization on SMT Processors Automatic Transformations for Communication-Minimized Parallelization and Locality Optimization in the Polyhedral Model

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:12:44.832981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation be779098-3f15-40ad-8f69-d1016595d602 · outbound

This paper cites Can Large Language Models Write Parallel Code?.

Accelerating Latency-Critical Applications with AI-Powered Semi-Automatic Fine-Grained Parallelization on SMT Processors Can Large Language Models Write Parallel Code?

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-08-05T13:12:44.651341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0dde186f-bbd4-4da9-ba26-a0ff3921c68a · outbound

This paper cites MonoCoder: Domain-Specific Code Language Model for HPC Codes and Tasks.

Accelerating Latency-Critical Applications with AI-Powered Semi-Automatic Fine-Grained Parallelization on SMT Processors MonoCoder: Domain-Specific Code Language Model for HPC Codes and Tasks

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-08-05T13:12:44.637198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f5cfbd26-3c48-452f-a49e-d01fda50cf17 · outbound

This paper cites OMPGPT: A Generative Pre-trained Transformer Model for OpenMP.

Accelerating Latency-Critical Applications with AI-Powered Semi-Automatic Fine-Grained Parallelization on SMT Processors OMPGPT: A Generative Pre-trained Transformer Model for OpenMP

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-08-05T13:12:44.622289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d717d99e-8e17-4e16-93c8-aaa5045b0c37 · outbound

This paper cites Should AI Optimize Your Code? A Comparative Study of Classical Optimizing Compilers Versus Current Large Language Models.

Accelerating Latency-Critical Applications with AI-Powered Semi-Automatic Fine-Grained Parallelization on SMT Processors Should AI Optimize Your Code? A Comparative Study of Classical Optimizing Compilers Versus Current Large Language Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-05T13:12:44.187916Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9f5d1c0a-d89b-4bd6-97fc-c327581785d0 · outbound

This paper cites Advising OpenMP Parallelization via a Graph-Based Approach with Transformers.

Accelerating Latency-Critical Applications with AI-Powered Semi-Automatic Fine-Grained Parallelization on SMT Processors Advising OpenMP Parallelization via a Graph-Based Approach with Transformers

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-08-05T13:12:44.596939Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6946559f-cab0-43bb-8c41-97d6afb88aab · outbound

This paper cites an unresolved cited work.

Accelerating Latency-Critical Applications with AI-Powered Semi-Automatic Fine-Grained Parallelization on SMT Processors Unresolved cited work

Reference 18

Resolution: unresolved; raw_fallback, observed 2026-08-05T13:12:44.821744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d3e67ead-bb7f-48be-a5f4-1a3c9c891cd8 · outbound

This paper cites Early performance evaluation of a.

Accelerating Latency-Critical Applications with AI-Powered Semi-Automatic Fine-Grained Parallelization on SMT Processors Early performance evaluation of a

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-05T13:12:44.198724Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 23d5e1de-3507-404b-a590-22f35d187495 · outbound

This paper cites Explo rin g the performance limits of simultaneous multithreading f o r m em or y intensive applications,.

Accelerating Latency-Critical Applications with AI-Powered Semi-Automatic Fine-Grained Parallelization on SMT Processors Explo rin g the performance limits of simultaneous multithreading f o r m em or y intensive applications,

Reference 20

Resolution: verified exact; doi, observed 2026-08-05T13:12:44.239994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 852a4af4-181c-46b5-a458-451e6fce9907 · outbound

This paper cites Simultaneous multithreading on x86_64 systems: an energy efficiency evaluation,.

Accelerating Latency-Critical Applications with AI-Powered Semi-Automatic Fine-Grained Parallelization on SMT Processors Simultaneous multithreading on x86_64 systems: an energy efficiency evaluation,

Reference 21

Resolution: metadata mismatch; raw_fallback, observed 2026-08-05T13:12:44.468585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a07c455d-234d-467c-a72c-f384f32a9bb5 · outbound

This paper cites RTRBench: A Benchmark Suite for Real -Time Robotics,.

Accelerating Latency-Critical Applications with AI-Powered Semi-Automatic Fine-Grained Parallelization on SMT Processors RTRBench: A Benchmark Suite for Real -Time Robotics,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-05T13:12:44.208428Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.