Architecture-Aware Optimization on a 1600-core Graphics Processor

The graphics processing unit (GPU) continues to make significant strides as an accelerator in commodity cluster computing for high-performance computing (HPC). For example, three of the top five fastest supercomputers in the world, as ranked by the TOP500, employ GPUs as accelerators. Despite this increasing interest in GPUs, however, optimizing the performance of a GPU-accelerated compute node requires deep technical knowledge of the underlying architecture. Although significant literature exists on how to optimize GPU performance on the more mature NVIDIA CUDA architecture, the converse is true for OpenCL on the AMD GPU. Consequently, we present and evaluate architecture-aware optimizations for the AMD GPU. The most prominent optimizations include (i) explicit use of registers, (ii) use of vector types, (iii) removal of branches, and (iv) use of image memory for global data. We demonstrate the efficacy of our AMD GPU optimizations by applying each optimization in isolation as well as in concert to a large-scale, molecular modeling application called GEM. Via these AMD-specific GPU optimizations, the AMD Radeon HD 5870 GPU delivers 65% better performance than with the wellknown NVIDIA-specific optimizations

Modeling the Resource Requirements of Convolutional Neural Networks on Mobile Devices

Convolutional Neural Networks (CNNs) have revolutionized the research in computer vision, due to their ability to capture complex patterns, resulting in high inference accuracies. However, the increasingly complex nature of these neural networks means that they are particularly suited for server computers with powerful GPUs. We envision that deep learning applications will be eventually and widely deployed on mobile devices, e.g., smartphones, self-driving cars, and drones. Therefore, in this paper, we aim to understand the resource requirements (time, memory) of CNNs on mobile devices. First, by deploying several popular CNNs on mobile CPUs and GPUs, we measure and analyze the performance and resource usage for every layer of the CNNs. Our findings point out the potential ways of optimizing the performance on mobile devices. Second, we model the resource requirements of the different CNN computations. Finally, based on the measurement, pro ling, and modeling, we build and evaluate our modeling tool, Augur, which takes a CNN configuration (descriptor) as the input and estimates the compute time and resource usage of the CNN, to give insights about whether and how e ciently a CNN can be run on a given mobile platform. In doing so Augur tackles several challenges: (i) how to overcome pro ling and measurement overhead; (ii) how to capture the variance in different mobile platforms with different processors, memory, and cache sizes; and (iii) how to account for the variance in the number, type and size of layers of the different CNN configurations