Mixing multi-core CPUs and GPUs for scientific simulation software

Recent technological and economic developments have led to widespread availability of multi-core CPUs and specialist accelerator processors such as graphical processing units (GPUs). The accelerated computational performance possible from these devices can be very high for some applications paradigms. Software languages and systems such as NVIDIA's CUDA and Khronos consortium's open compute language (OpenCL) support a number of individual parallel application programming paradigms. To scale up the performance of some complex systems simulations, a hybrid of multi-core CPUs for coarse-grained parallelism and very many core GPUs for data parallelism is necessary. We describe our use of hybrid applica- tions using threading approaches and multi-core CPUs to control independent GPU devices. We present speed-up data and discuss multi-threading software issues for the applications level programmer and o er some suggested areas for language development and integration between coarse-grained and ne-grained multi-thread systems. We discuss results from three common simulation algorithmic areas including: partial di erential equations; graph cluster metric calculations and random number generation. We report on programming experiences and selected performance for these algorithms on: single and multiple GPUs; multi-core CPUs; a CellBE; and using OpenCL. We discuss programmer usability issues and the outlook and trends in multi-core programming for scienti c applications developers

Parallelization of cycle-based logic simulation

Verification of digital circuits by Cycle-based simulation can be performed in parallel. The parallel implementation requires two phases: the compilation phase, that sets up the data needed for the execution of the simulation, and the simulation phase, that consists in executing the parallel simulation of the considered circuit for a certain number of cycles. During the early phase of design, compilation phase has to be repeated each time a bug is found. Thus, if the time of the compilation phase is too high, the advantages stemming from the parallel approach may be lost. In this work we propose an effective version of the compilation phase and compute the corresponding execution time. We also analyze the percentage of execution time required by the different steps of the compilation phase for a set of literature benchmarks. Further, we implemented the simulation phase exploiting the GPU architecture, and we computed the execution times for a set of benchmarks obtaining values comparable with literature ones. Finally, we implemented the sequential version of the Cycle-based simulation in such a way that the execution time is optimized. We used the sequential values to compute the speedup of the parallel version for the considered set of benchmarks

Galactos: Computing the Anisotropic 3-Point Correlation Function for 2 Billion Galaxies

The nature of dark energy and the complete theory of gravity are two central questions currently facing cosmology. A vital tool for addressing them is the 3-point correlation function (3PCF), which probes deviations from a spatially random distribution of galaxies. However, the 3PCF's formidable computational expense has prevented its application to astronomical surveys comprising millions to billions of galaxies. We present Galactos, a high-performance implementation of a novel, O(N^2) algorithm that uses a load-balanced k-d tree and spherical harmonic expansions to compute the anisotropic 3PCF. Our implementation is optimized for the Intel Xeon Phi architecture, exploiting SIMD parallelism, instruction and thread concurrency, and significant L1 and L2 cache reuse, reaching 39% of peak performance on a single node. Galactos scales to the full Cori system, achieving 9.8PF (peak) and 5.06PF (sustained) across 9636 nodes, making the 3PCF easily computable for all galaxies in the observable universe.Comment: 11 pages, 7 figures, accepted to SuperComputing 201

A scalable H-matrix approach for the solution of boundary integral equations on multi-GPU clusters

In this work, we consider the solution of boundary integral equations by means of a scalable hierarchical matrix approach on clusters equipped with graphics hardware, i.e. graphics processing units (GPUs). To this end, we extend our existing single-GPU hierarchical matrix library hmglib such that it is able to scale on many GPUs and such that it can be coupled to arbitrary application codes. Using a model GPU implementation of a boundary element method (BEM) solver, we are able to achieve more than 67 percent relative parallel speed-up going from 128 to 1024 GPUs for a model geometry test case with 1.5 million unknowns and a real-world geometry test case with almost 1.2 million unknowns. On 1024 GPUs of the cluster Titan, it takes less than 6 minutes to solve the 1.5 million unknowns problem, with 5.7 minutes for the setup phase and 20 seconds for the iterative solver. To the best of the authors' knowledge, we here discuss the first fully GPU-based distributed-memory parallel hierarchical matrix Open Source library using the traditional H-matrix format and adaptive cross approximation with an application to BEM problems

Learning from minimally labeled data with accelerated convolutional neural networks

The main objective of an Artificial Vision Algorithm is to design a mapping function that takes an image as an input and correctly classifies it into one of the user-determined categories. There are several important properties to be satisfied by the mapping function for visual understanding. First, the function should produce good representations of the visual world, which will be able to recognize images independently of pose, scale and illumination. Furthermore, the designed artificial vision system has to learn these representations by itself. Recent studies on Convolutional Neural Networks (ConvNets) produced promising advancements in visual understanding. These networks attain significant performance upgrades by relying on hierarchical structures inspired by biological vision systems. In my research, I work mainly in two areas: 1) how ConvNets can be programmed to learn the optimal mapping function using the minimum amount of labeled data, and 2) how these networks can be accelerated for practical purposes. In this work, algorithms that learn from unlabeled data are studied. A new framework that exploits unlabeled data is proposed. The proposed framework obtains state-of-the-art performance results in different tasks. Furthermore, this study presents an optimized streaming method for ConvNets’ hardware accelerator on an embedded platform. It is tested on object classification and detection applications using ConvNets. Experimental results indicate high computational efficiency, and significant performance upgrades over all other existing platforms