high performance computing on graphics processing units: hgpu.org

Choosing an appropriate programming paradigm for high-performance computing on low-power devices can be useful to speed up calculations. Many Android devices have an integrated GPU and – although not officially supported – the OpenCL framework can be used on Android devices for addressing these GPUs. OpenCL supports thread and data parallelism. Applications that use the […]

In this work, we analyze the performance of neural networks on a variety of heterogenous platforms. We strive to find the best platform in terms of raw benchmark performance, performance per watt and performance per Euro. To reach this goal, we focused on convolutional neural networks and created several micro- and macrobenchmark applications and used […]

The abstract relation between hardware parameters and program performance makes setting program parameters a difficult task. Without autotuning, software can miss low-level optimizations, resulting in lower performance. Traditionally, time-consuming trial and error search methods have been the staple of autotuning. Applying Natural language processing (NLP) based machine learning (ML) methods to source code as a […]

Finding optimal parameter configurations for tunable GPU kernels is a non-trivial exercise for large search spaces, even when automated. This poses an optimization task on a non-convex search space, using an expensive to evaluate function with unknown derivative. These characteristics make a good candidate for Bayesian Optimization, which has not been applied to this problem […]

We present Atos, a task-parallel GPU dynamic scheduling framework that is especially suited to dynamic irregular applications. Compared to the dominant Bulk Synchronous Parallel (BSP) frameworks, Atos exposes additional concurrency by supporting task-parallel formulations of applications with relaxed dependencies, achieving higher GPU utilization, which is particularly significant for problems with concurrency bottlenecks. Atos also offers […]

We propose a novel high-level approach for software development on GPU using Vulkan API. Our goal is to speed-up development and performance studies for complex algorithms on GPU, which is quite difficult and laborious for Vulkan due to large number of HW features low level details. The proposed approach uses auto programming to translate ordinary […]

FPGAs have been around for over 30 years and are a viable accelerator for compute-intensive workloads on HPC systems. The adoption of FPGAs for scientific applications has been stimulated recently by the emergence of better programming environments such as High-Level Synthesis (HLS) and OpenCL available through the Xilinx SDSoC design tool. The mapping of the […]

GPU compilers are complex software programs with many optimizations specific to target hardware. These optimizations are often controlled by heuristics hand-designed by compiler experts using time- and resource-intensive processes. In this paper, we developed a GPU compiler autotuning framework that uses off-policy deep reinforcement learning to generate heuristics that improve the frame rates of graphics […]

The high degree of parallelism and relatively complicated synchronization mechanisms in GPUs make writing correct kernels difficult. Data races pose one such concurrency correctness challenge, and therefore, effective methods of detecting as many data races as possible are required. Predictive partial order relations for CPU programs aim to expose data races that can be hidden […]

The standard RSA relies on multiple big-number modular exponentiation operations and longer key-length is required for better protection. This imposes a hefty time penalty for encryption and decryption. In this study, we analyzed and developed an improved parallel algorithm (PMKRSA) based on the idea of splitting the plaintext into multiple chunks and encrypt the chunks […]

Deep neural networks (DNNs) exploit many layers and a large number of parameters to achieve excellent performance. The training process of DNN models generally handles large-scale input data with many sparse features, which incurs high Input/Output (IO) cost, while some layers are compute-intensive. The training process generally exploits distributed computing resources to reduce training time. […]

An autotuner takes a parameterized code as input and tries to optimize the code by finding the best possible values for a given architecture. To our knowledge, there are currently no standardized benchmark suites for comparing and testing autotuners. Developers of autotuners thus make their own when presenting and comparing autotuners. We thus present BAT, […]