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The recent introduction of Unified Virtual Memory (UVM) in GPUs offers a new programming model that allows GPUs and CPUs to share the same virtual memory space, which shifts the complex memory management from programmers to GPU driver/ hardware and enables kernel execution even when memory is oversubscribed. Meanwhile, UVM may also incur considerable performance overhead due to tracking and data migration along with special handling of page faults and page table walk. As UVM is attracting significant attention from the research community to develop innovative solutions to these problems, in this paper, we propose a comprehensive UVM benchmark suite named UVMBench to facilitate future research on this important topic. The proposed UVMBench consists of 32 representative benchmarks from a wide range of application domains. The suite also features unified programming implementation and diverse memory access patterns across benchmarks, thus allowing thorough evaluation and comparison with current state-of-the-art. A set of experiments have been conducted on real GPUs to verify and analyze the benchmark suite behaviors under various scenarios. 

Throughput-oriented many-core processors demand highly efficient network-on-chip (NoC) architecture for data transferring. Recent advent of silicon interposer, stacked memory and 2.5D integration have further increased data transfer rate. This greatly intensifies traffic bottleneck in the NoC but, at the same time, also brings a significant new opportunity in utilizing wiring resources in the interposer. In this paper, we propose a novel concept called Equivalent Injection Routers (EIRs) which, together with interposer links, transform the few-to-many traffic pattern to many-to-many pattern, thus fundamentally solving the bottleneck problem. We have developed EquiNox as a design example. We utilize N-Queen and Monte Carlo Tree Search (MCTS) methods to help select EIRs by considering comprehensively from topological, architectural and physical aspects. Evaluation results show that, compared with prior work, the proposed EquiNox is able to reduce execution time by 23.5%, energy consumption by 18.9%, and EDP by 32.8%, under similar hardware cost. 

Architectural optimizations in general-purpose graphics processing units (GPGPUs) often exploit workload characteristics to reduce power and latency while improving performance. This paper finds, however, that prevailing assumptions about GPGPU traffic pattern characterization are inaccurate. These assumptions must therefore be re-evaluated, and more appropriate new patterns must be identified. This paper proposes a methodology to classify GPGPU traffic patterns, combining a convolutional neural network (CNN) for feature extraction and a t-distributed stochastic neighbor embedding (t-SNE) algorithm to determine traffic pattern clusters. A traffic pattern dataset is generated from common GPGPU benchmarks, transformed using heat mapping, and iteratively refined to ensure appropriate and highly accurate labels. The proposed classification model achieves 98.8% validation accuracy and 94.24% test accuracy. Furthermore, traffic in 96.6% of examined kernels can be classified into the eight identified traffic pattern categories. 

Since the advent of the horseshoe priors for regularisation, global–local shrinkage methods have proved to be a fertile ground for the development of Bayesian methodology in machine learning, specifically for high‐dimensional regression and classification problems. They have achieved remarkable success in computation and enjoy strong theoretical support. Most of the existing literature has focused on the linear Gaussian case; for which systematic surveys are available. The purpose of the current article is to demonstrate that the horseshoe regularisation is useful far more broadly, by reviewing both methodological and computational developments in complex models that are more relevant to machine learning applications. Specifically, we focus on methodological challenges in horseshoe regularisation in non‐linear and non‐Gaussian models, multivariate models and deep neural networks. We also outline the recent computational developments in horseshoe shrinkage for complex models along with a list of available software implementations that allows one to venture out beyond the comfort zone of the canonical linear regression problems.