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PolyDL
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/3-540-61736-1openalex-snapshot
The Data Parallel Programming Model
inferredcites →https://doi.org/10.1007/3-540-61736-1_44openalex-snapshot
Automatic parallelization in the polytope model
inferredcites →https://doi.org/10.1007/978-3-642-15582-6_49openalex-snapshot
isl: An Integer Set Library for the Polyhedral Model
inferredcites →https://doi.org/10.1016/s0898-1221(96)90463-0openalex-snapshot
Advances in neural information processing systems 7
inferredcites →https://doi.org/10.1109/cgo.2007.22openalex-snapshot
Loop Optimization using Hierarchical Compilation and Kernel Decomposition
inferredcites →https://doi.org/10.1109/cgo.2019.8661197openalex-snapshot
Tiramisu: A Polyhedral Compiler for Expressing Fast and Portable Code
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/hpca47549.2020.00015openalex-snapshot
SIGMA: A Sparse and Irregular GEMM Accelerator with Flexible Interconnects for DNN Training
inferredcites →https://doi.org/10.1109/ipdps.2009.5161054openalex-snapshot
A scalable auto-tuning framework for compiler optimization
inferredcites →https://doi.org/10.1109/ipdps47924.2020.00032openalex-snapshot
Harnessing Deep Learning via a Single Building Block
inferredcites →https://doi.org/10.1109/sc.2004.65openalex-snapshot
Using Information from Prior Runs to Improve Automated Tuning Systems
inferredcites →https://doi.org/10.1109/sc.2010.14openalex-snapshot
Combined Iterative and Model-driven Optimization in an Automatic Parallelization Framework
inferredcites →https://doi.org/10.1109/sc.2016.83openalex-snapshot
LIBXSMM: Accelerating Small Matrix Multiplications by Runtime Code Generation
inferredcites →https://doi.org/10.1145/1250734.1250780openalex-snapshot
Parameterized tiled loops for free
inferredcites →https://doi.org/10.1145/1356052.1356053openalex-snapshot
Anatomy of high-performance matrix multiplication
inferredcites →https://doi.org/10.1145/1379022.1375594openalex-snapshot
Iterative optimization in the polyhedral model
inferredcites →https://doi.org/10.1145/1542275.1542301openalex-snapshot
Parametric multi-level tiling of imperfectly nested loops
inferredcites →https://doi.org/10.1145/1772954.1772983openalex-snapshot
Parameterized tiling revisited
inferredcites →https://doi.org/10.1145/1854273.1854317openalex-snapshot
A model for fusion and code motion in an automatic parallelizing compiler
inferredcites →https://doi.org/10.1145/2185520.2185528openalex-snapshot
Decoupling algorithms from schedules for easy optimization of image processing pipelines
inferredcites →https://doi.org/10.1145/3079856.3080246openalex-snapshot
In-Datacenter Performance Analysis of a Tensor Processing Unit
inferredcites →https://doi.org/10.1145/3157733openalex-snapshot
Design of a High-Performance GEMM-like Tensor–Tensor Multiplication
inferredcites →https://doi.org/10.1145/3158120openalex-snapshot
Analytical modeling of cache behavior for affine programs
inferredcites →https://doi.org/10.1145/3211346.3211354openalex-snapshot
Diesel: DSL for linear algebra and neural net computations on GPUs
inferredcites →https://doi.org/10.1145/3306346.3322967openalex-snapshot
Learning to optimize halide with tree search and random programs
inferredcites →https://doi.org/10.1145/3314221.3314606openalex-snapshot
A fast analytical model of fully associative caches
inferredcites →https://doi.org/10.1145/3314221.3314653openalex-snapshot
Model-driven transformations for multi- and many-core CPUs
inferredcites →https://doi.org/10.1145/93542openalex-snapshot
Proceedings of the ACM SIGPLAN 1990 conference on Programming language design and implementation - PLDI '90
inferredcites →https://doi.org/10.1177/1094342013493939openalex-snapshot
Adaptive parallel tiled code generation and accelerated auto-tuning
inferredcites →https://doi.org/10.1177/1094342019866247openalex-snapshot
SWIRL: High-performance many-core CPU code generation for deep neural networks
inferredcites →https://doi.org/10.13140/rg.2.1.1351.9846openalex-snapshot
Mapping the Intel Last-Level Cache
inferredcites →https://doi.org/10.4230/lipics.cosit.2022.18openalex-snapshot
AI-Assisted Pipeline for Dynamic Generation of Trustworthy Health Supplement Content at Scale
inferredcites →https://doi.org/10.48550/arxiv.1802.04730openalex-snapshot
Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions
inferredcites →https://doi.org/10.48550/arxiv.1802.04799openalex-snapshot
TVM: An Automated End-to-End Optimizing Compiler for Deep Learning
inferredcites →https://doi.org/10.5555/3314872.3314896openalex-snapshot
Tiramisu: a polyhedral compiler for expressing fast and portable code
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/cpe.70164openalex-snapshot
Fusing the Polyhedral and Tensor Compilers to Accelerate Scientific Computing Kernels
inferred← citeshttps://doi.org/10.1016/j.sysarc.2025.103359openalex-snapshot
GTA: Generating high-performance tensorized program with dual-task scheduling
inferred← citeshttps://doi.org/10.1088/1742-6596/2906/1/012013openalex-snapshot
Research on uncovering parallelism in perfect loop nests for GCC compiler
inferred← citeshttps://doi.org/10.1109/access.2024.3402326openalex-snapshot
STuning-DL: Model-Driven Autotuning of Sparse GPU Kernels for Deep Learning
inferred← citeshttps://doi.org/10.1109/asp-dac58780.2024.10473863openalex-snapshot
Quantization-aware Optimization Approach for CNNs Inference on CPUs
inferred← citeshttps://doi.org/10.1109/cgo53902.2022.9741260openalex-snapshot
Optimizing GPU Deep Learning Operators with Polyhedral Scheduling Constraint Injection
inferred← citeshttps://doi.org/10.1109/cgo57630.2024.10444871openalex-snapshot
oneDNN Graph Compiler: A Hybrid Approach for High-Performance Deep Learning Compilation
inferred← citeshttps://doi.org/10.1109/ipdps57955.2024.00089openalex-snapshot
Harnessing Deep Learning and HPC Kernels via High-Level Loop and Tensor Abstractions on CPU Architectures
inferred← citeshttps://doi.org/10.1109/isca59077.2024.00076openalex-snapshot
Soter: Analytical Tensor-Architecture Modeling and Automatic Tensor Program Tuning for Spatial Accelerators
inferred← citeshttps://doi.org/10.1109/tc.2024.3398424openalex-snapshot
TensorMap: A Deep RL-Based Tensor Mapping Framework for Spatial Accelerators
inferred← citeshttps://doi.org/10.1109/tcad.2025.3629566openalex-snapshot
HAOT: Heterogeneous Hardware-Aware Tensor Computation Optimization Framework via Transformer
inferred← citeshttps://doi.org/10.1145/3434402openalex-snapshot
On the Anatomy of Predictive Models for Accelerating GPU Convolution Kernels and Beyond
inferred← citeshttps://doi.org/10.1145/3447818.3462213openalex-snapshot
A practical tile size selection model for affine loop nests
inferred← citeshttps://doi.org/10.1145/3545008.3545031openalex-snapshot
Automatically Generating High-performance Matrix Multiplication Kernels on the Latest Sunway Processor
inferred← citeshttps://doi.org/10.1145/3570305openalex-snapshot
YaConv: Convolution with Low Cache Footprint
inferred← citeshttps://doi.org/10.1145/3579990.3580015openalex-snapshot
Accelerating Deep Neural Networks on Mobile Multicore NPUs
inferred← citeshttps://doi.org/10.1145/3617232.3624858openalex-snapshot
Optimizing Deep Learning Inference via Global Analysis and Tensor Expressions
inferred← citeshttps://doi.org/10.1145/3712285.3759898openalex-snapshot
Constraint-Driven Auto-Tuning of GEMM-like Operators for MT-3000 Many-core Processor
inferred← citeshttps://doi.org/10.48550/arxiv.2104.05573openalex-snapshot
AI Powered Compiler Techniques for DL Code Optimization
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