25937

Posts

Dec, 5

Autotuning CUDA: Applying NLP Techniques to LS-CAT

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 […]
Dec, 5

Bayesian Optimization for auto-tuning GPU kernels

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 […]
Dec, 5

Atos: A Task-Parallel GPU Dynamic Scheduling Framework for Dynamic Irregular Computations

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 […]
Dec, 5

An Auto-Programming Approach to Vulkan

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 […]
Nov, 28

Concurrency Mapping to FPGAs with OpenCL: A Case Study with a Shallow Water Kernel

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 […]
Nov, 28

Generating GPU Compiler Heuristics using Reinforcement Learning

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 […]
Nov, 28

Predictive Data Race Detection for GPUs

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 […]
Nov, 28

A Variant RSA Acceleration with Parallelization

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 […]
Nov, 28

HeterPS: Distributed Deep Learning With Reinforcement Learning Based Scheduling in Heterogeneous Environments

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. […]
Nov, 21

BAT: A Benchmark suite for AutoTuners

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, […]
Nov, 21

Programming Heterogeneous Systems with General and Domain-Specific Frameworks

As chip manufacturing processes are getting ever closer to what is physically possible, the projections made by Moore’s Law and Dennard Scaling no longer hold true, and CPU performance has been stagnating over the last decade. At the same time, the performance requirements of many important application areas, ranging from machine learning to scientific computing, […]
Nov, 21

Accelerating JPEG Decompression on GPUs

The JPEG compression format has been the standard for lossy image compression for over multiple decades, offering high compression rates at minor perceptual loss in image quality. For GPU-accelerated computer vision and deep learning tasks, such as the training of image classification models, efficient JPEG decoding is essential due to limitations in memory bandwidth. As […]

* * *

* * *

* * *

HGPU group © 2010-2022 hgpu.org

All rights belong to the respective authors

Contact us: