Code and Data Transformations An Affine Scheduling Framework for Integrating Data Layout and Loop Transformations -- Guiding Code Optimizations with Deep Learning-Based Code Matching -- Expanding Opportunities for Array Privatization in Sparse Computations -- OpenMP and Fortran Concurrent Execution of Deferred OpenMP Target Tasks with Hidden Helper Threads -- Using Hardware Transactional Memory to Implement Speculative Privatization in OpenMP -- Improving Fortran Performance Portability -- Domain Specific Compilation COMET: A Domain-Specic Compilation of High-Performance Computational Chemistry -- G-Code Re-compilation and Optimization for Faster 3D Printing -- Li Machine Language and Quantum Computing Optimized Code Generation for Deep Neural Networks -- Thermal-Aware Compilation of Spiking Neural Networks to Neuromorphic Hardware -- A Quantum-Inspired Model For Bit-Serial SIMD-Parallel Computation -- Performance Analysis Enhancing the Top-Down Microarchitectural Analysis Method Using Purchasing Power Parity Theory -- Code Generation Cain: Automatic Code Generation for Simultaneous Convolutional Kernels on Focal-plane Sensor-processors -- Reordering Under the ECMAScript Memory Consistency Model -- Verication of Vectorization of Signal Transforms.

This book constitutes the thoroughly refereed post-conference proceedings of the 33rd International Workshop on Languages and Compilers for Parallel Computing, LCPC 2020, held in Stony Brook, NY, USA, in October 2020. Due to COVID-19 pandemic the conference was held virtually. The 15 revised full papers were carefully reviewed and selected from 19 submissions. The contributions were organized in topical sections named as follows: Code and Data Transformations; OpenMP and Fortran; Domain Specific Compilation; Machine Language and Quantum Computing; Performance Analysis; Code Generation.