Motivation
- 数据管理差距
- 数据库推理性能差距:1.缓存不命中 2.查询解析、优化和编译开销 3.其他开销
- 端到端的性能理解差距
现有决策树平台
- Scikit-learn: 对于RandomForest预测,采用模型并行(每个线程负责对输入数据运行一部分树的推理,结果会更新到一个由锁保护的共享结果向量),predict函数支持向量化,可以批量处理输入样本
- ONNX: 并行化策略(根据输入样本数量和森林中树的数量来选择:数据并行、模型并行)、不使用向量化处理(树遍历函数每次只处理单个样本)
- HummingBird:
- TreeLite: 将森林中的树分割成多个编译单元,将编译单元转换为C源代码函数,每个编译单元对应一个共享库文件
- Nvidia FIL: 每个GPU线程负责在一棵树上推理一批样本,采用批处理方式提高并行效率
- lleaves: 将树编译为嵌套的if-else块,不是直接生成C源代码
- Apache SparkSQL: 调用MLlib APIs来实现,在数据帧上运行决策森林推理
- TensorFlow Decision Forest(TFDF)
- PostgresML
The DICT Performance Model
D: 数据集大小 I: 推理吞吐量 C: 数据集的基数 T: 数据传输吞吐量
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
Compute-Intensive
Automatic Exploration
Loop Fusion
Data Movement
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
Optimization
Operation Fusion
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