Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis

Deep Neural Networks (DNNs) are becoming an important tool in modern computing applications. Accelerating their training is a major challenge and techniques range from distributed algorithms to low-level circuit design. In this survey, we describe the problem from a theoretical perspective, followed by approaches for its parallelization. We present trends in DNN architectures and the resulting implications on parallelization strategies. We then review and model the different types of concurrency in DNNs: from the single operator, through parallelism in network inference and training, to distributed deep learning. We discuss asynchronous stochastic optimization, distributed system architectures, communication schemes, and neural architecture search. Based on those approaches, we extrapolate potential directions for parallelism in deep learning

Bridging Control-Centric and Data-Centric Optimization

With the rise of specialized hardware and new programming languages, code optimization has shifted its focus towards promoting data locality. Most production-grade compilers adopt a control-centric mindset - instruction-driven optimization augmented with scalar-based dataflow - whereas other approaches provide domain-specific and general purpose data movement minimization, which can miss important control-flow optimizations. As the two representations are not commutable, users must choose one over the other. In this paper, we explore how both control- and data-centric approaches can work in tandem via the Multi-Level Intermediate Representation (MLIR) framework. Through a combination of an MLIR dialect and specialized passes, we recover parametric, symbolic dataflow that can be optimized within the DaCe framework. We combine the two views into a single pipeline, called DCIR, showing that it is strictly more powerful than either view. On several benchmarks and a real-world application in C, we show that our proposed pipeline consistently outperforms MLIR and automatically uncovers new optimization opportunities with no additional effort.Comment: CGO'2

Big Data causing Big (TLB) Problems: Taming Random Memory Accesses on the GPU

GPUs are increasingly adopted for large-scale database processing, where data accesses represent the major part of the computation. If the data accesses are irregular, like hash table accesses or random sampling, the GPU performance can suffer. Especially when scaling such accesses beyond 2GB of data, a performance decrease of an order of magnitude is encountered. This paper analyzes the source of the slowdown through extensive micro-benchmarking, attributing the root cause to the Translation Lookaside Buffer (TLB). Using the micro-benchmarks, the TLB hierarchy and structure are fully analyzed on two different GPU architectures, identifying never-before-published TLB sizes that can be used for efficient large-scale application tuning. Based on the gained knowledge, we propose a TLB-conscious approach to mitigate the slowdown for algorithms with irregular memory access. The proposed approach is applied to two fundamental database operations - random sampling and hash-based grouping - showing that the slowdown can be dramatically reduced, and resulting in a performance increase of up to 13×

Learning Combinatorial Node Labeling Algorithms

We present a graph neural network to learn graph coloring heuristics using reinforcement learning. Our learned deterministic heuristics give better solutions than classical degree-based greedy heuristics and only take seconds to evaluate on graphs with tens of thousands of vertices. As our approach is based on policy-gradients, it also learns a probabilistic policy as well. These probabilistic policies outperform all greedy coloring baselines and a machine learning baseline. Our approach generalizes several previous machine-learning frameworks, which applied to problems like minimum vertex cover. We also demonstrate that our approach outperforms two greedy heuristics on minimum vertex cover