LoopTune: Optimizing Tensor Computations with Reinforcement Learning
Advanced compiler technology is crucial for enabling machine learning applications to run on novel hardware, but traditional compilers fail to deliver performance, popular auto-tuners have long search times and expert-optimized libraries introduce unsustainable costs. To address this, we developed LoopTune, a deep reinforcement learning compiler that optimizes tensor computations in deep learning models for the CPU. LoopTune optimizes tensor traversal order while using the ultra-fast lightweight code generator LoopNest to perform hardware-specific optimizations. With a novel graph-based representation and action space, LoopTune speeds up LoopNest by 3.2x, generating an order of magnitude faster code than TVM, 2.8x faster than MetaSchedule, and 1.08x faster than AutoTVM, consistently performing at the level of the hand-tuned library Numpy. Moreover, LoopTune tunes code in order of seconds.
Code (0)
등록된 구현이 없습니다.
Tasks
CPUDeep Reinforcement Learningreinforcement-learningReinforcement LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Quantum Circuit Optimization with AlphaTensor
A key challenge in realizing fault-tolerant quantum computers is circuit optimization. Focusing on the most expensive gates in fault-tolerant quantum computation (namely, the T gates), we address the problem of T-count o…
Deep Reinforcement LearningTensor DecompositionDiscovering faster matrix multiplication algorithms with reinforcement learning
Improving the efficiency of algorithms for fundamental computations can have a widespread impact, as it can affect the overall speed of a large amount of computations. Matrix multiplication is one such primitive task, oc…
Deep Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)TensorIR: An Abstraction for Automatic Tensorized Program Optimization
Deploying deep learning models on various devices has become an important topic. The wave of hardware specialization brings a diverse set of acceleration primitives for multi-dimensional tensor computations. These new ac…
BIG-bench Machine LearningDeep LearningAtari games and Intel processors
The asynchronous nature of the state-of-the-art reinforcement learning algorithms such as the Asynchronous Advantage Actor-Critic algorithm, makes them exceptionally suitable for CPU computations. However, given the fact…
Atari GamesBIG-bench Machine LearningCPUDeep Reinforcement Learning+4Reinforcement Learning for Optimizing Large Qubit Array based Quantum Sensor Circuits
As the number of qubits in a sensor increases, the complexity of designing and controlling the quantum circuits grows exponentially. Manually optimizing these circuits becomes infeasible. Optimizing entanglement distribu…
Quantum Machine LearningReinforcement Learning