high performance computing on graphics processing units: hgpu.org

As data movement operations and power-budget become key bottlenecks in the design of computing systems, the interest in unconventional approaches such as processing-in-memory (PIM) and machine learning (ML), especially neural network (NN) based accelerators has grown significantly. Resistive RAM (ReRAM) is a promising technology for efficiently architecting PIM and NN based accelerators due to its […]

Streaming text processing is important for transforming and analyzing the rapidly growing data in modern society. Unfortunately, text processing software written using the sequential byte-at-a-time processing model fails to take full advantage of the resources available on modern processors for many reasons, including significant branch misprediction penalties due to the input-dependent branching structures of text […]

We implement exact triangle counting in graphs on the GPU using three different methodologies: subgraph matching to a triangle pattern; programmable graph analytics, with a set-intersection approach; and a matrix formulation based on sparse matrix-matrix multiplies. All three deliver best-of-class performance over CPU implementations and over comparable GPU implementations, with the graph-analytic approach achieving the […]

We propose and evaluate a novel strategy for tuning the performance of a class of stencil computations on Graphics Processing Units. The strategy uses a machine learning model to predict the optimal way to load data from memory followed by a heuristic that divides other optimizations into groups and exhaustively explores one group at a […]

Blockchain is a distributed ledger system based on P2P network and originally used for a crypto currency system. The P2P network of Blockchain is maintained by full nodes which are in charge of verifying all the transactions in the network. However, most Blockchain user nodes do not act as full nodes, because workload of full […]

We present a C++ library, TLoops, which uses a hierarchy of expression templates to represent operations upon tensorial quantities in single lines of C++ code that resemble analytic equations. These expressions may be run as-is, but may also be used to emit equivalent low-level C or CUDA code, which either performs the operations more quickly […]

Project page: BrainSlug: Transparent Neural Network Acceleration (http://www.brainslug.info/)   Neural network frameworks such as PyTorch and TensorFlow are the workhorses of numerous machine learning applications ranging from object recognition to machine translation. While these frameworks are versatile and straightforward to use, the training of and inference in deep neural networks is resource (energy, compute, and […]

Branch predictor (BP) is an essential component in modern processors since high BP accuracy can improve performance and reduce energy by decreasing the number of instructions executed on wrong-path. However, reducing latency and storage overhead of BP while maintaining high accuracy presents significant challenges. In this paper, we present a survey of dynamic branch prediction […]

Recent results at the Large Hadron Collider (LHC) have pointed to enhanced physics capabilities through the improvement of the real-time event processing techniques. Machine learning methods are ubiquitous and have proven to be very powerful in LHC physics, and particle physics as a whole. However, exploration of the use of such techniques in low-latency, low-power […]

Neural networks have been successfully used in many applications. Due to their computational complexity, it is difficult to implement them on embedded devices. Neural networks are inherently approximate and thus can be simplified. In this paper, CANNA proposes a gradual training approximation which adaptively sets the level of hardware approximation depending on the neural network’s […]

This paper presents an open-source enforcement learning toolkit named CytonRL. The toolkit implements four recent advanced deep Q-learning algorithms from scratch using C++ and NVIDIA’s GPU-accelerated libraries. The code is simple and elegant, owing to an open-source general-purpose neural network library named CytonLib. Benchmark shows that the toolkit achieves competitive performances on the popular Atari […]

NVIDIA cuDNN is a low-level library that provides GPU kernels frequently used in deep learning. Specifically, cuDNN implements several equivalent convolution algorithms, whose performance and memory footprint may vary considerably, depending on the layer dimensions. When an algorithm is automatically selected by cuDNN, the decision is performed on a per-layer basis, and thus it often […]