Paper Citation Record · LEDGER

KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads

As of 8 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 1 inbound Pith citation observation for arXiv:2505.21661.

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

pith.paper-citation-record.v1
2505.21661 v1

Coverage vector

measured 54 of 54 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:30:25.024913Z

measured 55 of 55 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T04:49:45.955512Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

54 of 54 outbound references displayed

verified exact1
verified fuzzy36
unresolved17
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2e344e02-b01f-4ccc-ba02-e4205fe72d85 · outbound

This paper cites AMD CDNA 3 Architec- ture, 2024.

KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads AMD CDNA 3 Architec- ture, 2024

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:34.500256Z

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Observation 2eb6d586-4189-48eb-b105-842caf372f90 · outbound

This paper cites AMD Instinct MI300.

KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads AMD Instinct MI300

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:34.344389Z

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Observation 73eeba50-5079-4895-9db1-e035931c4531 · outbound

This paper cites Composable kernel (CK) library, 2024.

KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads Composable kernel (CK) library, 2024

Reference 3

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Observation e39d64f4-f18f-440c-9dfa-b1e9d0d2d5b1 · outbound

This paper cites ROCm ROCProfiler, 2024.

KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads ROCm ROCProfiler, 2024

Reference 4

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Observation 27487e3b-0f90-4d32-8a29-b566a845e40f · outbound

This paper cites Version 6.2.4.

KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads Version 6.2.4

Reference 5

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Observation 290f3855-9c35-42cf-9d0e-12e558601a81 · outbound

This paper cites rocBLAS Library, 2023.

KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads rocBLAS Library, 2023

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:33.571161Z

Source-reported events for the cited work

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Observation 57e3bd36-2b97-4be7-a9c8-1f223975e3b9 · outbound

This paper cites Pytorch 2: Faster machine learning through dynamic python bytecode transformation and graph compilation.

KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads Pytorch 2: Faster machine learning through dynamic python bytecode transformation and graph compilation

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:33.368927Z

Source-reported events for the cited work

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Observation 7bfcd8d2-17c6-40eb-8dd6-42d58241f38d · outbound

This paper cites Cu- daDMA: optimizing GPU memory bandwidth via warp specialization.

KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads Cu- daDMA: optimizing GPU memory bandwidth via warp specialization

Reference 8

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Observation 9d5e68d6-583b-4ea5-ac8b-3fed454b7474 · outbound

This paper cites Hatchet: Pruning the overgrowth in parallel profiles.

KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads Hatchet: Pruning the overgrowth in parallel profiles

Reference 9

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Observation 56db27a6-5958-4258-9476-5d40c9cf77d1 · outbound

This paper cites JAX: com- posable transformations of Python+NumPy programs, 2018.

KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads JAX: com- posable transformations of Python+NumPy programs, 2018

Reference 10

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Observation af6e44c1-3fa5-454f-84a2-9114f38f6aa6 · outbound

This paper cites Language Models are Few-Shot Learners.

KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads Language Models are Few-Shot Learners

Reference 11

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Observation 12e87fc3-18af-4c55-805c-fde39d1251fd · outbound

This paper cites FLUX: Fast Software-based Communication Overlap On GPUs Through Kernel Fusion.

KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads FLUX: Fast Software-based Communication Overlap On GPUs Through Kernel Fusion

Reference 12

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Observation e8ee2ef3-9316-4ea0-9c1e-03a139372fb6 · outbound

This paper cites {TVM}: An automated {End-to-End} optimizing compiler for deep learning.

KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads {TVM}: An automated {End-to-End} optimizing compiler for deep learning

Reference 13

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Source-reported events for the cited work

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Observation da1d23e9-33a1-403a-a7c4-9197248d0658 · outbound

This paper cites Learning to optimize tensor programs.

KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads Learning to optimize tensor programs

Reference 14

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Observation 70948b4b-64fa-4f76-b913-09282b3312ca · outbound

This paper cites Nvidia hopper h100 gpu: Scaling per- formance.

KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads Nvidia hopper h100 gpu: Scaling per- formance

Reference 15

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Observation e515ed3f-c5ed-4351-bed5-824f17cb5415 · outbound

This paper cites V olta: Performance and programmability.

KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads V olta: Performance and programmability

Reference 16

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Observation cd36a80a-8a84-469d-86a2-a8d1bd62fd9a · outbound

This paper cites Crago, Sana Damani, Karthikeyan Sankar- alingam, and Stephen W.

KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads Crago, Sana Damani, Karthikeyan Sankar- alingam, and Stephen W

Reference 17

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Observation 2d5df09b-e791-4c35-9034-b1f53d7aa60e · outbound

This paper cites Davidson and Christopher W.

KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads Davidson and Christopher W

Reference 18

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Observation 8d485b2e-9f76-4a37-907d-48a7880ec5fd · outbound

This paper cites Chrome trace format, 2023.

KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads Chrome trace format, 2023

Reference 19

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Observation e8743293-0cba-417c-822f-db090b7c51ba · outbound

This paper cites Amanda: Unified instrumentation 14 framework for deep neural networks.

KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads Amanda: Unified instrumentation 14 framework for deep neural networks

Reference 20

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Observation 053317d4-7d5c-477a-9b90-252097da8bc3 · outbound

This paper cites Profile inference revisited.

KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads Profile inference revisited

Reference 21

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Observation 29bd1027-93f8-4c83-bb31-db15c772544e · outbound

This paper cites ALCOP: Automatic Load-Compute Pipelining in Deep Learning Compiler for AI-GPUs.

KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads ALCOP: Automatic Load-Compute Pipelining in Deep Learning Compiler for AI-GPUs

Reference 22

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Observation b05945cf-638c-494d-8c03-0b328d61116f · outbound

This paper cites Alcop: Automatic load- compute pipelining in deep learning compiler for ai- gpus.

KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads Alcop: Automatic load- compute pipelining in deep learning compiler for ai- gpus

Reference 23

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Observation 6b3fb28a-fef5-4fcb-b20c-20866a858629 · outbound

This paper cites Multi- physics simulations: Challenges and opportunities.

KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads Multi- physics simulations: Challenges and opportunities

Reference 24

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Observation a73110ad-2825-4370-bfb5-f1d1099a928e · outbound

This paper cites Llvm: A compilation framework for lifelong program analysis & transforma- tion.

KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads Llvm: A compilation framework for lifelong program analysis & transforma- tion

Reference 25

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Observation 54c58660-8eea-4202-b3ad-11c340ed26d5 · outbound

This paper cites Mlir: Scaling compiler infrastructure for do- main specific computation.

KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads Mlir: Scaling compiler infrastructure for do- main specific computation

Reference 26

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Observation 8c6437fb-9148-44fb-94f3-3bdf8c6f3095 · outbound

This paper cites Deep learning.

KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads Deep learning

Reference 27

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KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads Unresolved cited work

Reference 28

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Observation e4235d67-0ff5-497d-82b3-0a5addda178c · outbound

This paper cites Experimental FlashAttention3 using Triton, 2024.

KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads Experimental FlashAttention3 using Triton, 2024

Reference 29

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Observation 6028930b-df31-4f74-bde3-fe4661c93150 · outbound

This paper cites NVIDIA Turing GPU Architec- ture Whitepaper, 2018.

KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads NVIDIA Turing GPU Architec- ture Whitepaper, 2018

Reference 30

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Observation 1d72d265-bef9-4ede-a978-766f10be2cac · outbound

This paper cites cuBLAS Library, 2023.

KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads cuBLAS Library, 2023

Reference 31

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Observation 7fa48589-8726-4e2d-b4ac-468851ed5b65 · outbound

This paper cites CUPTI: CUDA Profiling Tools Interface, 2023.

KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads CUPTI: CUDA Profiling Tools Interface, 2023

Reference 32

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Observation 416fd099-5bf4-47c1-9099-65b41182c32b · outbound

This paper cites NVIDIA Nsight Compute, 2024.

KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads NVIDIA Nsight Compute, 2024

Reference 33

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Observation c2cfa9d0-71d2-477a-a996-c9d8ae0684f6 · outbound

This paper cites NVIDIA Nsight Systems, 2024.

KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads NVIDIA Nsight Systems, 2024

Reference 34

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Observation 7cbfb932-243c-45f5-9250-329661b90689 · outbound

This paper cites NVIDIA PTX, 2024.

KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads NVIDIA PTX, 2024

Reference 35

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Observation 58fa46a6-32fb-41a2-85af-35dc37be549e · outbound

This paper cites Group GEMM in Triton, 2024.

KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads Group GEMM in Triton, 2024

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:29.065653Z

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Observation e41f3c2e-4d9e-486e-8919-ac13e92c36c3 · outbound

This paper cites Optimizing distributed ml communi- cation with fused computation-collective operations.

KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads Optimizing distributed ml communi- cation with fused computation-collective operations

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:28.909368Z

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Observation 8410f4dd-7f2c-4774-88fb-74a2d3637376 · outbound

This paper cites PyTorch Profiler.

KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads PyTorch Profiler

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:28.804814Z

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Observation 9e33af1c-37ca-4ab3-a77f-a84c89132027 · outbound

This paper cites Reinventing High Performance Computing: Challenges and Opportunities.

KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads Reinventing High Performance Computing: Challenges and Opportunities

Reference 39

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Observation 01376597-3a37-4828-babc-6bb0cb7c485f · outbound

This paper cites Learning representations by back-propagating errors.

KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads Learning representations by back-propagating errors

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:23.775785Z

Source-reported events for the cited work

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Observation 4fe7920d-610c-4afb-bda8-6fad7fb2be62 · outbound

This paper cites Flashattention- 3: Fast and accurate attention with asynchrony and low- precision.

KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads Flashattention- 3: Fast and accurate attention with asynchrony and low- precision

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:28.685775Z

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Observation c01853a3-027a-4e52-85d3-a2a427ea60cd · outbound

This paper cites FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision.

KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:23.937127Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 759b5c7d-5f43-4bac-8b38-274054b48cc6 · outbound

This paper cites Tensor program opti- mization with probabilistic programs.

KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads Tensor program opti- mization with probabilistic programs

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:23.995310Z

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Observation feb87269-0c5b-4cd8-ae0f-061a40d6246d · outbound

This paper cites ThunderKittens: Simple, Fast, and Adorable AI Kernels.

KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads ThunderKittens: Simple, Fast, and Adorable AI Kernels

Reference 44

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 274c38bf-249b-455c-8760-e2c0a40f8074 · outbound

This paper cites CUTLASS, January 2023.

KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads CUTLASS, January 2023

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:28.475954Z

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Observation ec4c7c0b-4745-4260-b614-767e1e5ad6b5 · outbound

This paper cites Large language models in medicine.

KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads Large language models in medicine

Reference 46

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Source-reported events for the cited work

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Observation 031bdfdc-2d9f-4c1e-b1d5-3976e281fe8a · outbound

This paper cites Triton: an intermediate language and compiler for tiled neural network computations.

KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads Triton: an intermediate language and compiler for tiled neural network computations

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:24.270842Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eee372a2-8b78-4873-bf88-f62c4503f10b · outbound

This paper cites Nvbit: A dynamic binary instru- mentation framework for nvidia gpus.

KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads Nvbit: A dynamic binary instru- mentation framework for nvidia gpus

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:27.955031Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 54438187-5bdc-4664-a4c7-a00147aabc7c · outbound

This paper cites WLB-LLM: Workload-Balanced 4D Parallelism for Large Language Model Training.

KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads WLB-LLM: Workload-Balanced 4D Parallelism for Large Language Model Training

Reference 49

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Observation f47efffa-1cd2-476b-bfb4-9dd2fcabe9bb · outbound

This paper cites Rap: Resource-aware automated gpu sharing for multi-gpu recommendation model training and input preprocessing.

KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads Rap: Resource-aware automated gpu sharing for multi-gpu recommendation model training and input preprocessing

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:27.714838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fcee688e-b778-446a-aaef-556320014db0 · outbound

This paper cites BloombergGPT: A Large Language Model for Finance.

KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads BloombergGPT: A Large Language Model for Finance

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:24.734854Z

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Observation 57ec749e-fe5d-4864-8b84-af6860c41538 · outbound

This paper cites Ansor: Generating {High-Performance} tensor programs for deep learn- ing.

KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads Ansor: Generating {High-Performance} tensor programs for deep learn- ing

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:24.804890Z

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Observation 9ab0e73d-bc02-4e5a-9de7-75afe7c1d6f3 · outbound

This paper cites Gvprof: A value profiler for gpu-based clusters.

KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads Gvprof: A value profiler for gpu-based clusters

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:27.405478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 70b31a9c-f283-484e-baa0-b258163575ea · outbound

This paper cites Valueexpert: Exploring value patterns in gpu-accelerated applications.

KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads Valueexpert: Exploring value patterns in gpu-accelerated applications

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:26.974974Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

Observation d12f834c-5d30-49cb-94e8-e4e59f0174e5 · inbound

TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters cites this paper.

TileSight: A First-Principles Tile-Centric Analytical GPU Performance Model from Cores to Clusters KPerfIR: Towards an Open and Compiler-centric Ecosystem for GPU Kernel Performance Tooling on Modern AI Workloads

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-01T04:49:45.955512Z

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Unavailable: canonical work link unavailable.