70 research outputs found

    Efficient Algorithms And Optimizations For Scientific Computing On Many-Core Processors

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    Designing efficient algorithms for many-core and multicore architectures requires using different strategies to allow for the best exploitation of the hardware resources on those architectures. Researchers have ported many scientific applications to modern many-core and multicore parallel architectures, and by doing so they have achieved significant speedups over running on single CPU cores. While many applications have achieved significant speedups, some applications still require more effort to accelerate due to their inherently serial behavior. One class of applications that has this serial behavior is the Monte Carlo simulations. Monte Carlo simulations have been used to simulate many problems in statistical physics and statistical mechanics that were not possible to simulate using Molecular Dynamics. While there are a fair number of well-known and recognized GPU Molecular Dynamics codes, the existing Monte Carlo ensemble simulations have not been ported to the GPU, so they are relatively slow and could not run large systems in a reasonable amount of time. Due to the previously mentioned shortcomings of existing Monte Carlo ensemble codes and due to the interest of researchers to have a fast Monte Carlo simulation framework that can simulate large systems, a new GPU framework called GOMC is implemented to simulate different particle and molecular-based force fields and ensembles. GOMC simulates different Monte Carlo ensembles such as the canonical, grand canonical, and Gibbs ensembles. This work describes many challenges in developing a GPU Monte Carlo code for such ensembles and how I addressed these challenges. This work also describes efficient many-core and multicore large-scale energy calculations for Monte Carlo Gibbs ensemble using cell lists. Designing Monte Carlo molecular simulations is challenging as they have less computation and parallelism when compared to similar molecular dynamics applications. The modified cell list allows for more speedup gains for energy calculations on both many-core and multicore architectures when compared to other implementations without using the conventional cell lists. The work presents results and analysis of the cell list algorithms for each one of the parallel architectures using top of the line GPUs, CPUs, and Intel’s Phi coprocessors. In addition, the work evaluates the performance of the cell list algorithms for different problem sizes and different radial cutoffs. In addition, this work evaluates two cell list approaches, a hybrid MPI+OpenMP approach and a hybrid MPI+CUDA approach. The cell list methods are evaluated on a small cluster of multicore CPUs, Intel Phi coprocessors, and GPUs. The performance results are evaluated using different combinations of MPI processes, threads, and problem sizes. Another application presented in this dissertation involves the understanding of the properties of crystalline materials, and their design and control. Recent developments include the introduction of new models to simulate system behavior and properties that are of large experimental and theoretical interest. One of those models is the Phase-Field Crystal (PFC) model. The PFC model has enabled researchers to simulate 2D and 3D crystal structures and study defects such as dislocations and grain boundaries. In this work, GPUs are used to accelerate various dynamic properties of polycrystals in the 2D PFC model. Some properties require very intensive computation that may involve hundreds of thousands of atoms. The GPU implementation has achieved significant speedups of more than 46 times for some large systems simulations

    Improving GPU performance : reducing memory conflicts and latency

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    Improving GPU performance : reducing memory conflicts and latency

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    Software Defined Radio Solutions for Wireless Communications Systems

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    Wireless technologies have been advancing rapidly, especially in the recent years. Design, implementation, and manufacturing of devices supporting the continuously evolving technologies require great efforts. Thus, building platforms compatible with different generations of standards and technologies has gained a lot of interest. As a result, software defined radios (SDRs) are investigated to offer more flexibility and scalability, and reduce the design efforts, compared to the conventional fixed-function hardware-based solutions.This thesis mainly addresses the challenges related to SDR-based implementation of today’s wireless devices. One of the main targets of most of the wireless standards has been to improve the achievable data rates, which imposes strict requirements on the processing platforms. Realizing real-time processing of high throughput signal processing algorithms using SDR-based platforms while maintaining energy consumption close to conventional approaches is a challenging topic that is addressed in this thesis.Firstly, this thesis concentrates on the challenges of a real-time software-based implementation for the very high throughput (VHT) Institute of Electrical and Electronics Engineers (IEEE) 802.11ac amendment from the wireless local area networks (WLAN) family, where an SDR-based solution is introduced for the frequency-domain baseband processing of a multiple-input multipleoutput (MIMO) transmitter and receiver. The feasibility of the implementation is evaluated with respect to the number of clock cycles and the consumed power. Furthermore, a digital front-end (DFE) concept is developed for the IEEE 802.11ac receiver, where the 80 MHz waveform is divided to two 40 MHz signals. This is carried out through time-domain digital filtering and decimation, which is challenging due to the latency and cyclic prefix (CP) budget of the receiver. Different multi-rate channelization architectures are developed, and the software implementation is presented and evaluated in terms of execution time, number of clock cycles, power, and energy consumption on different multi-core platforms.Secondly, this thesis addresses selected advanced techniques developed to realize inband fullduplex (IBFD) systems, which aim at improving spectral efficiency in today’s congested radio spectrum. IBFD refers to concurrent transmission and reception on the same frequency band, where the main challenge to combat is the strong self-interference (SI). In this thesis, an SDRbased solution is introduced, which is capable of real-time mitigation of the SI signal. The implementation results show possibility of achieving real-time sufficient SI suppression under time-varying environments using low-power, mobile-scale multi-core processing platforms. To investigate the challenges associated with SDR implementations for mobile-scale devices with limited processing and power resources, processing platforms suitable for hand-held devices are selected in this thesis work. On the baseband processing side, a very long instruction word (VLIW) processor, optimized for wireless communication applications, is utilized. Furthermore, in the solutions presented for the DFE processing and the digital SI canceller, commercial off-the-shelf (COTS) multi-core central processing units (CPUs) and graphics processing units (GPUs) are used with the aim of investigating the performance enhancement achieved by utilizing parallel processing.Overall, this thesis provides solutions to the challenges of low-power, and real-time software-based implementation of computationally intensive signal processing algorithms for the current and future communications systems

    High Level Synthesis and Evaluation of an Automotive RADAR Signal Processing algorithm for FPGAs

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    High Level Synthesis (HLS) is a technology used to design and develop hardware (HW) using high-level languages such as C/C++. An HLS model of an automotive RADAR signal processing algorithm has been developed for the purpose of comparison between the HLS model and the existing HDL model. Register Transfer Level (RTL) programming is a technology used to design and develop hardware at the register transfer level (or low level) using Hardware description languages such as Verilog and VHDL. FPGA development usually requires the knowledge of RTL technologies. HLS gives software (SW) developers the ability to design and implement their designs on an FPGA without requiring the knowledge of RTL technologies and HDL. Even though HLS is currently gaining popularity, the applications used to evaluate HLS tend to remain small. We synthesize an automotive RADAR signal processing system using HLS-based design methodology, which has mid to high complexity, and compare our synthesis results to that of the RTL-based design. We used many techniques used to make the high-level program model ready for synthesis while optimizing for both speed and resource usage using Xilinx Vivado HLS Computer-Aided Design (CAD) tool. We achieved a speed up of 2X compared to the RTL-based design while reducing the design time from approximately 16 weeks to 6 weeks. The FPGA resource utilization increased but it was still under 5% of the total resources available on the FPGA

    Cross layer reliability estimation for digital systems

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    Forthcoming manufacturing technologies hold the promise to increase multifuctional computing systems performance and functionality thanks to a remarkable growth of the device integration density. Despite the benefits introduced by this technology improvements, reliability is becoming a key challenge for the semiconductor industry. With transistor size reaching the atomic dimensions, vulnerability to unavoidable fluctuations in the manufacturing process and environmental stress rise dramatically. Failing to meet a reliability requirement may add excessive re-design cost to recover and may have severe consequences on the success of a product. %Worst-case design with large margins to guarantee reliable operation has been employed for long time. However, it is reaching a limit that makes it economically unsustainable due to its performance, area, and power cost. One of the open challenges for future technologies is building ``dependable'' systems on top of unreliable components, which will degrade and even fail during normal lifetime of the chip. Conventional design techniques are highly inefficient. They expend significant amount of energy to tolerate the device unpredictability by adding safety margins to a circuit's operating voltage, clock frequency or charge stored per bit. Unfortunately, the additional cost introduced to compensate unreliability are rapidly becoming unacceptable in today's environment where power consumption is often the limiting factor for integrated circuit performance, and energy efficiency is a top concern. Attention should be payed to tailor techniques to improve the reliability of a system on the basis of its requirements, ending up with cost-effective solutions favoring the success of the product on the market. Cross-layer reliability is one of the most promising approaches to achieve this goal. Cross-layer reliability techniques take into account the interactions between the layers composing a complex system (i.e., technology, hardware and software layers) to implement efficient cross-layer fault mitigation mechanisms. Fault tolerance mechanism are carefully implemented at different layers starting from the technology up to the software layer to carefully optimize the system by exploiting the inner capability of each layer to mask lower level faults. For this purpose, cross-layer reliability design techniques need to be complemented with cross-layer reliability evaluation tools, able to precisely assess the reliability level of a selected design early in the design cycle. Accurate and early reliability estimates would enable the exploration of the system design space and the optimization of multiple constraints such as performance, power consumption, cost and reliability. This Ph.D. thesis is devoted to the development of new methodologies and tools to evaluate and optimize the reliability of complex digital systems during the early design stages. More specifically, techniques addressing hardware accelerators (i.e., FPGAs and GPUs), microprocessors and full systems are discussed. All developed methodologies are presented in conjunction with their application to real-world use cases belonging to different computational domains
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