The Machine Learning Systems Laboratory works at the intersection of machine learning and high-performance computing. It develops parallel processing techniques that accelerate the training of large deep learning models on distributed-memory systems and shared-memory multiprocessor platforms. For modern GPUs it designs matrix and tensor algorithms with strengthened data locality (GEMM, SpMM, SpGEMM, convolution, tensor contraction and others), and studies model compression alongside them.