high performance computing on graphics processing units: hgpu.org

QMCPACK is an open source quantum Monte Carlo package for ab-initio electronic structure calculations. It supports calculations of metallic and insulating solids, molecules, atoms, and some model Hamiltonians. Implemented real space quantum Monte Carlo algorithms include variational, diffusion, and reptation Monte Carlo. QMCPACK uses Slater-Jastrow type trial wave functions in conjunction with a sophisticated optimizer […]

The 2018 International Workshop on Embedded Multicore Systems to be held in conjunction with the 47th International Conference on Parallel Processing (ICPP 2018) https://sites.google.com/view/icpp-ems2018/ Embedded systems with multicore designs are of major focuses from both industry and academia. While embedded multicore systems will look to play an important role ahead for system designs, many challenging […]

We would like to invite you to contribute to and participate in 2018 5th International Conference on Electrical and Electronics Engineering (ICEEE 2018), which will be held in Istanbul, Turkey during May 3-5, 2018. ICEEE 2018 is a forum for presenting excellent results and new challenges facing the field of the reliability and availability of […]

Hardware acceleration is considered a powerful tool in parallel-computing, able to overcome the limitations imposed by sequential execution of software applications and, at the same time, provide energy-efficient alternatives to other parallel computing platforms such as GPUs. However, the increasing application complexity makes it unaffordable to map algorithms directly into HDL. Hence, High-Level Synthesis tools […]

Modern partial differential equation (PDE) models across scientific disciplines require sophisticated numerical methods resulting in complex codes as well as large numbers of simulations for analysis like parameter studies and uncertainty quantification. To evaluate the behavior of the model for sufficeintly long times, for instance, to compare to laboratory time scales, often requires long-time simulations […]

Although the performance of commodity computers has improved drastically with the introduction of multicore processors and GPU computing, the standard R distribution is still based on single-threaded model of computation, using only a small fraction of the computational power available now for most desktops and laptops. Modern statistical software packages rely on high performance implementations […]

Efficient implementations of HPC applications for parallel architectures generally rely on external software packages (e.g., BLAS, LAPACK, CUDNN). While these libraries provide highly optimized routines for certain characteristics of inputs (e.g., square matrices), they generally do not retain optimal performance across the wide range of problems encountered in practice. In this paper, we present an […]

Parallel architectures, including multi-core processors, many-core processors, and multi-node systems, have become commonplace, as it is no longer feasible to improve single-core performance through increasing its operating clock frequency. Furthermore, to keep up with the exponentially growing desire for more and more computational power, the number of cores/nodes in parallel architectures has continued to dramatically […]

Due to their high density and near-zero leakage power consumption, non-volatile memories (NVMs) are promising candidates for designing future memory systems. However, compared to conventional memories, NVMs also face more-severe security threats, e.g., the limited write endurance of NVMs makes them vulnerable to write-attacks. Also, the non-volatility of NVMs allows the data to persist even […]

Nowadays, most computer systems are equipped with powerful parallel devices such as Graphics Processing Units (GPUs). They are present in almost every computer system including mobile devices, tablets, desktop computers and servers. These parallel systems have unlocked the possibility for many scientists and companies to process significant amounts of data in shorter time. But the […]

Scalable frameworks, such as TensorFlow, MXNet, Caffe, and PyTorch drive the current popularity and utility of deep learning. However, these frameworks are optimized for a narrow range of server-class GPUs and deploying workloads to other platforms such as mobile phones, embedded devices, and specialized accelerators (e.g., FPGAs, ASICs) requires laborious manual effort. We propose TVM, […]

To most efficiently utilize modern parallel architectures, the memory access patterns of algorithms must make heavy use of the cache architecture: successively accessed data must be close in memory (spatial locality) and one piece of data must be reused as many times as possible (temporal locality). In this work we analyse the performance of unstructured […]