high performance computing on graphics processing units: hgpu.org

This paper describes AutoParBench, a framework to test OpenMP-based automatic parallelization tools. The core idea of this framework is a common representation, called a "JSON snapshot", that normalizes the output produced by auto-parallelizers. By converting—automatically—this output to the common representation, AutoParBench lets us compare auto-parallelizers among themselves, and compare them semantically against a reference collection. […]

Writing efficient programs for heterogeneous platforms is challenging: programmers must deal with multiple programming models, partition work for CPUs and accelerators with different compute capabilities, requiring different amounts of parallelism, and manage memory in multiple distinct address spaces. Consequently, programming models which only require expressing parallelism and data dependences can not only unburden the programmer […]

The use of reduced precision to improve performance metrics such as computation latency and power consumption is a common practice in the embedded systems field. This practice is emerging as a new trend in High Performance Computing (HPC), especially when new error-tolerant applications are considered. However, standard compiler frameworks do not support automated precision customization, […]

We present the GPU version of DeePMD-kit, which, upon training a deep neural network model using ab initio data, can drive extremely large-scale molecular dynamics (MD) simulation with ab initio accuracy. Our tests show that the GPU version is 7 times faster than the CPU version with the same power consumption. The code can scale […]

An install-less, header-only library which is a loosely-coupled collection of utility functions and classes for writing device-side CUDA code (kernels and non-kernel functions). These let us: * Write templated device-side without constantly coming up against not-trivially-templatable bits. * Use standard-library(-like) containers in device-side code (but not have to use them). * Not repeat ourselves as […]

Recent years have seen an increase of heterogeneous architectures combining multi-core CPUs with accelerators such as GPU, FPGA, and Intel Xeon Phi. GPU can achieve significant performance for certain categories of application. Nevertheless, achieving this performance with low-level APIs (e.g. CUDA, OpenCL) requires to rewrite the sequential code, to have a good knowledge of GPU […]

Convolution operations are essential constituents of convolutional neural networks. Their efficient and performance-portable implementation demands tremendous programming effort and fine-tuning. Winograd’s minimal filtering algorithm is a well-known method to reduce the computational complexity of convolution operations. Unfortunately, existing implementations of this algorithm are either vendor-specific or hard-coded to support a small subset of convolutions, thus […]

GPUs are a key enabler of the revolution in machine learning and high performance computing, functioning as de facto co-processors to accelerate large-scale computation. As the programming stack and tool support have matured, GPUs have also become accessible to programmers, who may lack detailed knowledge of the underlying architecture and fail to fully leverage the […]

FPGAs have found increasing adoption in data center applications since a new generation of high-level tools have become available which noticeably reduce development time for FPGA accelerators and still provide high quality of results. There is however no high-level benchmark suite available which specifically enables a comparison of FPGA architectures, programming tools and libraries for […]

The Cpp-Taskflow project addresses the long-standing question: How can we make it easier for developers to write parallel and heterogeneous programs with high performance and simultaneous high productivity? Cpp-Taskflow develops a simple and powerful task programming model to enable efficient implementations of heterogeneous decomposition strategies. Our programming model empowers users with both static and dynamic […]

Object detection is a technology that deals with recognizing classes of objects and their location. It is used in many different areas, such as in face-detecting systems [16, 34, 37], surveillance tools [9], human-machine interfaces [17], and self-driving cars [18, 23, 25, 26, 30]. These days, deep learning object detection approaches have achieved significantly better […]

Tensor computation plays a paramount role in a broad range of domains, including machine learning, data analytics, and scientific computing. The wide adoption of tensor computation and its huge computation cost has led to high demand for flexible, portable, and high-performance library implementation on heterogeneous hardware accelerators such as GPUs and FPGAs. However, the current […]