optimized Open source, automated end-to- end optimization framework for deep learning.TVM Stack High-Level Differentiable IR Tensor Expression and Optimization Search Space LLVM, CUDA, Metal VTA Edge Edge FPGA Cloud FPGA ASIC Optimization AutoTVM Device FleetExisting Deep Learning Frameworks High-level data flow graph Hardware Primitive Tensor operators such as Conv2D eg. cuDNN Offload to FrameworksLimitations of Existing Approach cuDNN Frameworks New operator introduced by operator fusion optimization potential benefit: 1.5x speedup Engineering intensiveMachine Learning based Program Optimizer

compute sin(tmp) in a separate loop, allocating a second array.] This loop fusion is not a compiler optimization that may or may not occur, it is a syntactic guarantee whenever nested f.(args...) calls are the method body. However, the actual caching behavior is an implementation-defined performance optimization, so it is invalid to depend too closely on this behavior. The number of times a generated function arguments. In this style of definition, the code generation feature is essentially an optional optimization. The compiler will use it if convenient, but otherwise may choose to use the normal implementation

compute sin(tmp) in a separate loop, allocating a second array.] This loop fusion is not a compiler optimization that may or may not occur, it is a syntactic guarantee whenever nested f.(args...) calls are the method body. However, the actual caching behavior is an implementation-defined performance optimization, so it is invalid to depend too closely on this behavior. The number of times a generated function arguments. In this style of definition, the code generation feature is essentially an optional optimization. The compiler will use it if convenient, but otherwise may choose to use the normal implementation

compute sin(tmp) in a separate loop, allocating a second array.] This loop fusion is not a compiler optimization that may or may not occur, it is a syntactic guarantee whenever nested f.(args...) calls are the method body. However, the actual caching behavior is an implementation-defined performance optimization, so it is invalid to depend too closely on this behavior. The number of times a generated function arguments. In this style of definition, the code generation feature is essentially an optional optimization. The compiler will use it if convenient, but otherwise may choose to use the normal implementation

compute sin(tmp) in a separate loop, allocating a second array.] This loop fusion is not a compiler optimization that may or may not occur, it is a syntactic guarantee whenever nested f.(args...) calls are the method body. However, the actual caching behavior is an implementation-defined performance optimization, so it is invalid to depend too closely on this behavior. The number of times a generated function arguments. In this style of definition, the code generation feature is essentially an optional optimization. The compiler will use it if convenient, but otherwise may choose to use the normal implementation

compute sin(tmp) in a separate loop, allocating a second array.] This loop fusion is not a compiler optimization that may or may not occur, it is a syntactic guarantee whenever nested f.(args...) calls are the method body. However, the actual caching behavior is an implementation-defined performance optimization, so it is invalid to depend too closely on this behavior. The number of times a generated function arguments. In this style of definition, the code generation feature is essentially an optional optimization. The compiler will use it if convenient, but otherwise may choose to use the normal implementation

compute sin(tmp) in a separate loop, allocating a second array.] This loop fusion is not a compiler optimization that may or may not occur, it is a syntactic guarantee whenever nested f.(args...) calls are the method body. However, the actual caching behavior is an implementation-defined performance optimization, so it is invalid to depend too closely on this behavior. The number of times a generated function arguments. In this style of definition, the code generation feature is essentially an optional optimization. The compiler will use it if convenient, but otherwise may choose to use the normal implementation

compute sin(tmp) in a separate loop, allocating a second array.] This loop fusion is not a compiler optimization that may or may not occur, it is a syntactic guarantee whenever nested f.(args...) calls are the method body. However, the actual caching behavior is an implementation-defined performance optimization, so it is invalid to depend too closely on this behavior. The number of times a generated function arguments. In this style of definition, the code generation feature is essentially an optional optimization. The compiler will use it if convenient, but otherwise may choose to use the normal implementation

compute sin(tmp) in a separate loop, allocating a second array.] This loop fusion is not a compiler optimization that may or may not occur, it is a syntactic guarantee whenever nested f.(args...) calls are the method body. However, the actual caching behavior is an implementation-defined performance optimization, so it is invalid to depend too closely on this behavior. The number of times a generated function arguments. In this style of definition, the code generation feature is essentially an optional optimization. The compiler will use it if convenient, but otherwise may choose to use the normal implementation

requirement of familiarity with WMMA API “Unified matmul schedule for GPU 。 Maintainability & Common Optimization Sharing 。 Search across the entire space (TensorCore + non-TensorCore) Our >olution wmma:mma_syncfcompute Jocalloj B_shareal_locollol A_sharea_locolloj compute_locallo族 了 了 Performance Optimization 计划了全事业部 “Same as non-TensorCore CUDA codegen 。Auto tune tiling sizes 。 Vectorized COMPUTING PLATFORM COMPUTING PLATFORM INT8 Inference on PAI- 引FTe[= PAI-Blade Model Analysis Graph optimization Blade Graph Optimizer TensorRT Customized OptimizeT TAO Compiler (XLA) cuUBLAS/VcuDNNVCUTL，