Operation Fusion Compute-Intensive Automatic Exploration Performance Model

MCFuser SC 2024

MCFuser High-Performance and Rapid Fusion of Memory-Bound Compute-Intensive Operators

Posted by Treaseven on March 7, 2025

Motivation

融合MBCI算子(内存受限计算密集算子)的挑战： 1.融合策略的搜索空间通常是不完整的 2.内存访问与计算循环的直接耦合会导致冗余的数据移动 3.融合策略受限于冗长的自动调优阶段和笨拙的搜索空间

MCFuser

search space generation and optimization

  • search space generation
  • memory access optimization
  • pruning:(1.去重 2.防止压倒性的中间张量的共享内存 3.避免过多展开 4.共享内存限制)

performance model and exploration

  • analytical performance model \(t_{estm} = (t_{mem} + t_{comp}) \times \alpha\)
\[t_{mem} = \sum_{S_{X_i} \in \{L_{X_i}\}+\{S_{X_i}\}} \frac{TS_{X_i} \times \prod_{l_j \in LP\_set(S)} l_j}{\mathcal{W}}\] \[t_{comp} = \sum_{S_{X_i} \in \{C_{X_i}\}} \frac{Fp_{X_i} \times \prod_{l_j \in LP\_set(S)} l_j}{\mathcal{P}}\] \[\alpha = (N_{block} + N_{SM})/N_{block}\]
  • exploration

implementation

  • intra-block optimization and code generation
  • framework and front-end

Evaluation

Reference

MCFuser: High-Performance and Rapid Fusion of Memory-Bound Compute-Intensive Operators

CATALOG
    FEATURED TAGS
    Tensor Compiler Compiler Optimization Code Generation Heterogeneous Systems Operator Fusion Deep Neural Network Recursive Tensor Execution Deep Learning Compiler Classical Machine Learning Compiler Optimizations Bayesian Optimization Autotuning Spatial Accelerators Tensor Computations Code Reproduction Neural Processing Units Polyhedral Model Auto-tuning Machine Learning Compiler Neural Network Program Transformations Tensor Programs Deep learning Tensor Program Optimizer Search Algorithm Compiler Infrastructure Scalalbe and Modular Compiler Systems Tensor Computation GPU Task Scheduling GPU Streams Tensor Expression Language Automated Program optimization Framework AI compiler memory hierarchy data locality tiling fusion polyhedral model scheduling domain-specific architectures memory intensive TVM Sparse Tensor Algebra Sparse Iteration Spaces Optimizing Transformations Tensor Operations Machine Learning Model Scoring AI Compiler Memory-Intensive Computation Fusion Neural Networks Dataflow Domain specific Language Programmable Domain-specific Acclerators Mapping Space Search Gradient-based Search Deep Learning Systems Systems for Machine Learning Programming Models Compilation Design Space Exploration Tile Size Optimization Performance Modeling High-Performance Tensor Program Tensor Language Model Tensor Expression GPU Loop Transformations Vectorization and Parallelization Hierarchical Classifier TVM API Optimizing Compilers Halide Pytorch Optimizing Tensor Programs Gradient Descent debug Automatic Tensor Program Tuning Operators Fusion Tensor Program Cost Model Weekly Schedule Spatio-temporal Schedule tensor compilers auto-tuning tensor program optimization compute schedules Tensor Compilers Data Processing Pipeline Mobile Devices Layout Transformations Transformer Design space exploration GPU kernel optimization Compilers Group Tuning Technique Tensor Processing Unit Hardware-software Codeisgn Data Analysis Adaptive Systems Program Auto-tuning python api Code Optimization Distributed Systems High Performance Computing code generation compiler optimization tensor computation Instructions Integration Code rewriting Tensor Computing DSL CodeReproduction Deep Learning Compiler Loop Program Analysis Nested Data Parallelism Loop Fusion C++ Machine Learning System Decision Forest Optimizfing Compiler Decision Tree Ensemble Decision Tree Inference Parallelization Optimizing Compiler decision trees random forest machine learning parallel processing multithreading Tree Structure Performance Model Code generation Compiler optimization Tensor computation accelerator neural networks optimizing compilers autotuning performance models deep neural networks compilers auto-scheduling tensor programs Tile size optimization Performance modeling Program Functionalization affine transformations loop optimization Performance Optimization Subgraph Similarity deep learning compiler Intra- and Inter-Operator Parallelisms ILP tile-size operator fusion cost model graph partition zero-shot tuning tensor program kernel orchestration machine learning compiler Loop tiling Locality Polyhedral compilation Optimizing Transformation Sparse Tensors Asymptotic Analysis Automatic Scheduling Data Movement Optimization Operation Fusion Compute-Intensive Automatic Exploration data reuse deep reuse Tensorize docker graph substitution compiler Just-in-time compiler graph Tensor program construction tensor compilation graph traversal Markov analysis Deep Learning Compilation Tensor Program Auto-Tuning Decision Tree Search-based code generation Domain specific lanuages Parallel architectures Dynamic neural network mobile device spatial accelerate software mapping reinforcement learning Computation Graph Graph Scheduling and Transformation Graph-level Optimization Operator-level Optimization Partitioning Algorithms IR Design Parallel programming languages Software performance Digitial signal processing Retargetable compilers Equational logic and rewriting Tensor-level Memory Management Code Generation and Optimizations Scheduling Sparse Tensor Auto-Scheduling Tensor Coarse-Grained Reconfigurable Architecture Graph Neural Network Reinforcement Learning Auto-Tuning Domain-Specific Accelerator Deep learning compiler Long context Memory optimization code analysis transformer architecture-mapping