high performance computing on graphics processing units: hgpu.org

Large knowledge bases (KBs) are useful for many AI tasks, but are difficult to integrate into modern gradient-based learning systems. Here we describe a framework for accessing soft symbolic database using only differentiable operators. For example, this framework makes it easy to conveniently write neural models that adjust confidences associated with facts in a soft […]

Sparse matrix-vector multiplication (SpMV) operations are commonly used in various scientific applications. The performance of the SpMV operation often depends on exploiting regularity patterns in the matrix. Various representations have been proposed to minimize the memory bandwidth bottleneck arising from the irregular memory access pattern involved. Among recent representation techniques, tensor decomposition is a popular […]

Recent technological advances have proliferated the available computing power, memory, and speed of modern Central Processing Units (CPUs), Graphics Processing Units (GPUs), and Field Programmable Gate Arrays (FPGAs). Consequently, the performance and complexity of Artificial Neural Networks (ANNs) is burgeoning. While GPU accelerated Deep Neural Networks (DNNs) currently offer state-of-the-art performance, they consume large amounts […]

Applications are migrating en masse to the cloud, while accelerators such as GPUs, TPUs, and FPGAs proliferate in the wake of Moore’s Law. These technological trends are incompatible. Cloud applications run on virtual platforms, but traditional I/O virtualization techniques have not provided production-ready solutions for accelerators. As a result, cloud providers expose accelerators by using […]

Deep neural networks (DNNs) are a key enabler of today’s intelligent applications and services. cuDNN is the de-facto standard library of deep learning primitives, which makes it easy to develop sophisticated DNN models. However, cuDNN is a propriatary software from NVIDIA, and thus does not allow the user to customize it based on her needs. […]

A prototype framework for formal, machinechecked validation of GPU pseudo-assembly code algorithms using the Coq proof assistant is presented and discussed. The framework is the first to afford GPU programmers a reliable means of formally machine-validating high-assurance GPU computations without trusting any specific source-to-assembly compilation toolchain. A formal operational semantics for the PTX pseudo-assembly language […]

Despite the fact that compiler technologies for automatic vectorization have been under development for over four decades, there are still considerable gaps in the capabilities of modern compilers to perform automatic vectorization for SIMD units. One such gap can be found in the handling of loops with dependence cycles that involve memory-based anti (write-after-read) and […]

Domain-specific languages (DSLs) play an increasingly important role in the generation of high performing software. They allow the user to exploit specific knowledge encoded in the constructs for the generation of code adapted to a particular hardware architecture; at the same time, they make it easier to generate optimized code for a multitude of platforms […]

In recent years there has been an extraordinary growth of the Internet of Things (IoT) and its protocols. The increasing diffusion of electronic devices with identification, computing and communication capabilities is laying ground for the emergence of a highly distributed service and networking environment. The above mentioned situation implies that there is an increasing demand […]

With the improvement in processing systems, machine learning applications are finding widespread use in almost all sectors of technology. Image recognition is one application of machine learning which has become widely popular with various architectures and systems aimed at improving recognition performance. With classification accuracy now approaching saturation point, many researchers are now focusing on […]

Tomographic image sizes keep increasing over time and while the GPUs that compute the tomographic reconstruction are also increasing in memory size, they are not doing so fast enough to reconstruct the largest datasets. This problem is often solved by reconstructing data in large clusters of GPUs with enough devices to fit the measured X-ray […]

Graphics processing units (GPUs) are being widely used as co-processors in many domains to accelerate general-purpose workloads that are data-parallel and computationally intensive, i.e., GPGPU. An emerging usage domain is adopting GPGPU to accelerate inherently computation-intensive Deep Neural Network (DNN) workloads in autonomous systems. Such autonomous systems are usually time-sensitive, especially for autonomous driving systems. […]