Paper Citation Record · LEDGER

QiMeng-TensorOp: Automatically Generating High-Performance Tensor Operators with Hardware Primitives

As of 23 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 2 inbound Pith citation observations for arXiv:2505.06302.

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

pith.paper-citation-record.v1
2505.06302 v1

Coverage vector

measured 49 of 49 reference resolution

Typed states for the displayed outbound observations.

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measured 51 of 51 standing notices

One-hop event checks from named stored sources.

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measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:34:04.652782Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T10:34:05.589341Z

Reference resolution

49 of 49 outbound references displayed

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External citation measurements

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Outbound references

Observation 2dbcf31a-de3d-4ba5-b5a7-b0b267b60031 · outbound

This paper cites Precision-energy-throughput scal- ing of generic matrix multiplication and discrete convolu- tion kernels via linear projections.

QiMeng-TensorOp: Automatically Generating High-Performance Tensor Operators with Hardware Primitives Precision-energy-throughput scal- ing of generic matrix multiplication and discrete convolu- tion kernels via linear projections

Reference 1

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Observation 4e71d887-c2bb-4e1a-b429-9e63b9021176 · outbound

This paper cites Nvidia A100 Tensor Core GPU: Performance and innovation.

QiMeng-TensorOp: Automatically Generating High-Performance Tensor Operators with Hardware Primitives Nvidia A100 Tensor Core GPU: Performance and innovation

Reference 8

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Observation 078d9e27-69e7-47a1-8c9f-9e71dbaae483 · outbound

This paper cites Claude 3.5 Sonnet.

QiMeng-TensorOp: Automatically Generating High-Performance Tensor Operators with Hardware Primitives Claude 3.5 Sonnet

Reference 9

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Observation d5ce0f7e-68d7-41b5-8317-d7308e20cd17 · outbound

This paper cites DeepSeek-V3 Technical Report.

QiMeng-TensorOp: Automatically Generating High-Performance Tensor Operators with Hardware Primitives DeepSeek-V3 Technical Report

Reference 11

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Observation 1cf2130d-582f-4c2e-b354-8f2a315c786a · outbound

This paper cites Flexible Performant GEMM Kernels on GPUs.

QiMeng-TensorOp: Automatically Generating High-Performance Tensor Operators with Hardware Primitives Flexible Performant GEMM Kernels on GPUs

Reference 12

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Observation a247c96e-2745-47d1-8462-b17bfa7e353b · outbound

This paper cites Tensorir: An abstraction for automatic tensorized program optimization.

QiMeng-TensorOp: Automatically Generating High-Performance Tensor Operators with Hardware Primitives Tensorir: An abstraction for automatic tensorized program optimization

Reference 13

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Observation 4a99e110-7f9a-4caa-a4d3-0949bf5eff74 · outbound

This paper cites The Llama 3 Herd of Models.

QiMeng-TensorOp: Automatically Generating High-Performance Tensor Operators with Hardware Primitives The Llama 3 Herd of Models

Reference 14

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Observation a7e55381-991b-4d36-ab0e-29f6d127aa54 · outbound

This paper cites Automatic generation of ARM NEON micro-kernels for matrix multiplication.

QiMeng-TensorOp: Automatically Generating High-Performance Tensor Operators with Hardware Primitives Automatic generation of ARM NEON micro-kernels for matrix multiplication

Reference 15

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Observation 7c0a71b1-da33-4b23-81cf-989ba57641fb · outbound

This paper cites Textbooks Are All You Need.

QiMeng-TensorOp: Automatically Generating High-Performance Tensor Operators with Hardware Primitives Textbooks Are All You Need

Reference 16

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Observation 85936515-9da2-41be-b5ed-ea8fd1b681b7 · outbound

This paper cites Deep Residual Learning for Image Recognition.

QiMeng-TensorOp: Automatically Generating High-Performance Tensor Operators with Hardware Primitives Deep Residual Learning for Image Recognition

Reference 17

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Observation 29677837-0684-499d-9317-d2ad4bce9a96 · outbound

This paper cites In-datacenter performance analysis of a tensor processing unit.

QiMeng-TensorOp: Automatically Generating High-Performance Tensor Operators with Hardware Primitives In-datacenter performance analysis of a tensor processing unit

Reference 20

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Observation ca501492-ed13-4231-bce1-311566df0905 · outbound

This paper cites Exploit- ing Intel® Advanced Matrix Extensions (AMX) for Large Language Model Inference.

QiMeng-TensorOp: Automatically Generating High-Performance Tensor Operators with Hardware Primitives Exploit- ing Intel® Advanced Matrix Extensions (AMX) for Large Language Model Inference

Reference 22

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Observation e0b4b441-d88a-4fd3-871c-a474488fa2bc · outbound

This paper cites oneAPI Open-Source Math Library Interface.

QiMeng-TensorOp: Automatically Generating High-Performance Tensor Operators with Hardware Primitives oneAPI Open-Source Math Library Interface

Reference 23

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Observation 76f4a096-07b3-4d0c-b5f1-a58cb1dfe12b · outbound

This paper cites Autotuning GEMM kernels for the Fermi GPU.

QiMeng-TensorOp: Automatically Generating High-Performance Tensor Operators with Hardware Primitives Autotuning GEMM kernels for the Fermi GPU

Reference 24

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Observation a1e57917-efd3-4c51-ba74-71f5605e314c · outbound

This paper cites Cambricon: An instruction set architecture for neural networks.

QiMeng-TensorOp: Automatically Generating High-Performance Tensor Operators with Hardware Primitives Cambricon: An instruction set architecture for neural networks

Reference 26

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Observation d368b010-54b1-46ec-8a82-81c184a557b0 · outbound

This paper cites Introducing Llama 3.1: Our most capable models to date.

QiMeng-TensorOp: Automatically Generating High-Performance Tensor Operators with Hardware Primitives Introducing Llama 3.1: Our most capable models to date

Reference 28

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Observation 99270a18-0545-47af-b6e1-17c6329ee676 · outbound

This paper cites ReACC: A Retrieval-Augmented Code Completion Framework.

QiMeng-TensorOp: Automatically Generating High-Performance Tensor Operators with Hardware Primitives ReACC: A Retrieval-Augmented Code Completion Framework

Reference 29

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Observation dd84b7c5-0477-4dff-b655-b956a10d0f5a · outbound

This paper cites NVIDIA Tensor Core Programmabil- ity, Performance & Precision.

QiMeng-TensorOp: Automatically Generating High-Performance Tensor Operators with Hardware Primitives NVIDIA Tensor Core Programmabil- ity, Performance & Precision

Reference 30

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Observation 1c61b06a-ecd3-4ddc-8407-362ffae22470 · outbound

This paper cites CUBLAS LIBRARY user guide v12.1.

QiMeng-TensorOp: Automatically Generating High-Performance Tensor Operators with Hardware Primitives CUBLAS LIBRARY user guide v12.1

Reference 31

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Observation e7b4d3c8-5337-410b-9b69-4d600cb9fdce · outbound

This paper cites [OpenAI, 2025] OpenAI.

QiMeng-TensorOp: Automatically Generating High-Performance Tensor Operators with Hardware Primitives [OpenAI, 2025] OpenAI

Reference 32

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Observation 1af83234-57db-430b-a16b-4b20c374cb66 · outbound

This paper cites U-Net: Convolutional Networks for Biomedical Image Segmentation,.

QiMeng-TensorOp: Automatically Generating High-Performance Tensor Operators with Hardware Primitives U-Net: Convolutional Networks for Biomedical Image Segmentation,

Reference 34

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Observation 937666da-9beb-4962-a93d-e22800cfe1d7 · outbound

This paper cites Code Llama: Open Foundation Models for Code.

QiMeng-TensorOp: Automatically Generating High-Performance Tensor Operators with Hardware Primitives Code Llama: Open Foundation Models for Code

Reference 35

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Observation 2568c05e-bf8b-44ce-bcd7-4b9c36560a4e · outbound

This paper cites Tensor program optimization with probabilistic pro- grams.

QiMeng-TensorOp: Automatically Generating High-Performance Tensor Operators with Hardware Primitives Tensor program optimization with probabilistic pro- grams

Reference 36

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This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

QiMeng-TensorOp: Automatically Generating High-Performance Tensor Operators with Hardware Primitives Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 37

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This paper cites Efficient processing of deep neural net- works: A tutorial and survey.

QiMeng-TensorOp: Automatically Generating High-Performance Tensor Operators with Hardware Primitives Efficient processing of deep neural net- works: A tutorial and survey

Reference 38

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This paper cites Fast implementation of DGEMM on Fermi GPU.

QiMeng-TensorOp: Automatically Generating High-Performance Tensor Operators with Hardware Primitives Fast implementation of DGEMM on Fermi GPU

Reference 39

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Observation a483d327-5d02-4316-9148-de81756764a2 · outbound

This paper cites Chain-of-Thought Prompting Elicits Rea- soning in Large Language Models.

QiMeng-TensorOp: Automatically Generating High-Performance Tensor Operators with Hardware Primitives Chain-of-Thought Prompting Elicits Rea- soning in Large Language Models

Reference 41

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Observation 5f5970df-732d-4d7a-98f6-6a4d21d991c1 · outbound

This paper cites The storage hierarchy is not a hierarchy: Optimiz- ing caching on modern storage devices with orthus.

QiMeng-TensorOp: Automatically Generating High-Performance Tensor Operators with Hardware Primitives The storage hierarchy is not a hierarchy: Optimiz- ing caching on modern storage devices with orthus

Reference 42

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Observation 27d3c2ea-8e12-40ee-9f8f-eefc3f201ec9 · outbound

This paper cites autoGEMM: Pushing the Limits of Irregular Matrix Mul- tiplication on Arm Architectures.

QiMeng-TensorOp: Automatically Generating High-Performance Tensor Operators with Hardware Primitives autoGEMM: Pushing the Limits of Irregular Matrix Mul- tiplication on Arm Architectures

Reference 43

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This paper cites Model-driven level 3 BLAS performance opti- mization on Loongson 3A processor.

QiMeng-TensorOp: Automatically Generating High-Performance Tensor Operators with Hardware Primitives Model-driven level 3 BLAS performance opti- mization on Loongson 3A processor

Reference 44

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Observation b6b38813-4ce7-40e9-9a1d-0e0254b8844a · outbound

This paper cites Hasco: Towards agile hardware and software co-design for tensor computation.

QiMeng-TensorOp: Automatically Generating High-Performance Tensor Operators with Hardware Primitives Hasco: Towards agile hardware and software co-design for tensor computation

Reference 45

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Observation e886e66c-4928-46d3-929b-521a4bd46c78 · outbound

This paper cites Large Language Models Meet NL2Code: A Survey.

QiMeng-TensorOp: Automatically Generating High-Performance Tensor Operators with Hardware Primitives Large Language Models Meet NL2Code: A Survey

Reference 46

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Observation 78f2a07b-c858-444b-9282-d1b6c39ef2f6 · outbound

This paper cites TLP: A Deep Learning-Based Cost Model for Tensor Pro- gram Tuning.

QiMeng-TensorOp: Automatically Generating High-Performance Tensor Operators with Hardware Primitives TLP: A Deep Learning-Based Cost Model for Tensor Pro- gram Tuning

Reference 47

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Observation e29177c9-be1c-4973-acf1-47480008f44c · outbound

This paper cites Enabling Tensor Language Model to Assist in Generating High-Performance Tensor Programs for Deep Learning.

QiMeng-TensorOp: Automatically Generating High-Performance Tensor Operators with Hardware Primitives Enabling Tensor Language Model to Assist in Generating High-Performance Tensor Programs for Deep Learning

Reference 48

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This paper cites Ansor: Generating High-Performance Ten- sor Programs for Deep Learning.

QiMeng-TensorOp: Automatically Generating High-Performance Tensor Operators with Hardware Primitives Ansor: Generating High-Performance Ten- sor Programs for Deep Learning

Reference 49

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Observation cc68d893-7766-472e-8c09-670f0556a0a3 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

QiMeng-TensorOp: Automatically Generating High-Performance Tensor Operators with Hardware Primitives LLaMA: Open and Efficient Foundation Language Models

Reference 2011

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Observation 0f6e40fc-b231-4712-80dd-6782e27a6f62 · outbound

This paper cites StarCoder: may the source be with you!.

QiMeng-TensorOp: Automatically Generating High-Performance Tensor Operators with Hardware Primitives StarCoder: may the source be with you!

Reference 2012

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This paper cites Multi-lingual Evaluation of Code Generation Models.

QiMeng-TensorOp: Automatically Generating High-Performance Tensor Operators with Hardware Primitives Multi-lingual Evaluation of Code Generation Models

Reference 2013

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This paper cites A new golden age for computer architecture.

QiMeng-TensorOp: Automatically Generating High-Performance Tensor Operators with Hardware Primitives A new golden age for computer architecture

Reference 2015

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Observation f5fc90e5-fb11-4be8-a73f-b4940708d20f · outbound

This paper cites High-Performance Tensor Learning Primitives Using GPU Tensor Cores.

QiMeng-TensorOp: Automatically Generating High-Performance Tensor Operators with Hardware Primitives High-Performance Tensor Learning Primitives Using GPU Tensor Cores

Reference 2016

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Observation d665f4be-373a-4e91-8cf6-48bc84dbfc97 · outbound

This paper cites High Performance GPU Code Generation for Matrix-Matrix Multiplication using MLIR: Some Early Results.

QiMeng-TensorOp: Automatically Generating High-Performance Tensor Operators with Hardware Primitives High Performance GPU Code Generation for Matrix-Matrix Multiplication using MLIR: Some Early Results

Reference 2017

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Observation 5e535f0d-b2f0-418a-be6e-867b4aa58450 · outbound

This paper cites Xuantie-910: A Commercial Multi- Core 12-Stage Pipeline Out-of-Order 64-bit High Per- formance RISC-V Processor with Vector Extension.

QiMeng-TensorOp: Automatically Generating High-Performance Tensor Operators with Hardware Primitives Xuantie-910: A Commercial Multi- Core 12-Stage Pipeline Out-of-Order 64-bit High Per- formance RISC-V Processor with Vector Extension

Reference 2018

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Observation 8337a8e4-6be7-4847-96be-b5661da30e86 · outbound

This paper cites Automatic Generation of Micro-kernels for Performance Portability of Matrix Multiplication on RISC-V Vector Processors.

QiMeng-TensorOp: Automatically Generating High-Performance Tensor Operators with Hardware Primitives Automatic Generation of Micro-kernels for Performance Portability of Matrix Multiplication on RISC-V Vector Processors

Reference 2019

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Observation 1baca3a5-41db-4662-ac39-62fef05596e0 · outbound

This paper cites Experiments and optimizations for TVM on RISC-V Architectures with P Extension.

QiMeng-TensorOp: Automatically Generating High-Performance Tensor Operators with Hardware Primitives Experiments and optimizations for TVM on RISC-V Architectures with P Extension

Reference 2020

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Observation 89cbbf1b-26d6-4480-8df6-fd28a40b36f3 · outbound

This paper cites Heron: Automatically Constrained High-Performance Li- brary Generation for Deep Learning Accelerators.

QiMeng-TensorOp: Automatically Generating High-Performance Tensor Operators with Hardware Primitives Heron: Automatically Constrained High-Performance Li- brary Generation for Deep Learning Accelerators

Reference 2021

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Observation 0f3d3397-4ea9-4b82-b7f4-e32807f6cc87 · outbound

This paper cites Program Synthesis with Large Language Models.

QiMeng-TensorOp: Automatically Generating High-Performance Tensor Operators with Hardware Primitives Program Synthesis with Large Language Models

Reference 2022

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This paper cites Tackling the Matrix Multiplication Micro-Kernel Generation with Exo.

QiMeng-TensorOp: Automatically Generating High-Performance Tensor Operators with Hardware Primitives Tackling the Matrix Multiplication Micro-Kernel Generation with Exo

Reference 2023

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Observation 75920c96-a955-4875-a855-39e35cad169b · outbound

This paper cites [Dally et al., 2021] William J Dally, Stephen W Keckler, and David B Kirk.

QiMeng-TensorOp: Automatically Generating High-Performance Tensor Operators with Hardware Primitives [Dally et al., 2021] William J Dally, Stephen W Keckler, and David B Kirk

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:21:45.448299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 0bb652b8-dc32-4cab-8794-ff74f2b22ab6 · outbound

This paper cites [Ragan et al., 2013] Jonathan Ragan, Connelly Barnes, and Andrew Adams et al.

QiMeng-TensorOp: Automatically Generating High-Performance Tensor Operators with Hardware Primitives [Ragan et al., 2013] Jonathan Ragan, Connelly Barnes, and Andrew Adams et al

Reference 2025

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:21:45.180811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Pith citing papers

Observation 0613ad5c-6843-4a3b-9358-e656ec082d4e · inbound

QiMeng: Fully Automated Hardware and Software Design for Processor Chip cites this paper.

QiMeng: Fully Automated Hardware and Software Design for Processor Chip QiMeng-TensorOp: Automatically Generating High-Performance Tensor Operators with Hardware Primitives

Reference 83

Resolution: verified exact; local_arxiv, observed 2026-08-07T10:34:05.595069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3be5b7e5-042b-4262-8c59-928d32faf87c · inbound

Towards Automated Kernel Generation in the Era of LLMs cites this paper.

Towards Automated Kernel Generation in the Era of LLMs QiMeng-TensorOp: Automatically Generating High-Performance Tensor Operators with Hardware Primitives

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-03T08:49:51.279853Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.