Accelerating host-compiled simulation by modifying IR code: industrial application in the spatial domain

Space applications rely on long and complex design processes, as they must deal with strict non-functional requirements such as criticality, timeliness, reliability and safety. The huge number of analysis and evaluations performed requires powerful simulations technologies combining high simulation speed and accuracy. Host-compiled simulation is a powerful approach to achieve fast, timed simulation of software running in complex embedded systems. However, in the general term, there is still the need of improving the speed and accuracy of these solutions, and there is a lack of host-compiled approaches oriented to space applications. To solve the first point, this paper presents an alternative that modifies the standard solution of adding the modeling of the cross-compiled control flow in the host computer by modifying the compiler's intermediate representation. That way, the host binary naturally follows the cross-compiled binary flow, avoiding a separate modeling, and improving simulation speed while maintaining accuracy. Additionally, the paper focuses on LEON processor, commonly used by the European Space Agency (ESA).This work has been funded by FEDER/Ministerio de Ciencia, Innovación y Universidades - Agencia Estatal de Investigación/ TEC2017-86722-C4-3-R and the EC through the FP7-JTI 621429 EMC2 project

Dynamic time management for improved accuracy and speed in host-compiled multi-core platform models

textWith increasing complexity and software content, modern embedded platforms employ a heterogeneous mix of multi-core processors along with hardware accelerators in order to provide high performance in limited power budgets. Due to complex interactions and highly dynamic behavior, static analysis of real-time performance and other constraints is challenging. As an alternative, full-system simulations have been widely accepted by designers. With traditional approaches being either slow or inaccurate, so-called host-compiled simulators have recently emerged as a solution for rapid evaluation of complete systems at early design stages. In such approaches, a faster simulation is achieved by natively executing application code at the source level, abstracting execution behavior of target platforms, and thus increasing simulation granularity. However, most existing host-compiled simulators often focus on application behavior only while neglecting effects of hardware/software interactions and associated speed and accuracy tradeoffs in platform modeling. In this dissertation, we focus on host-compiled operating system (OS) and processor modeling techniques, and we introduce novel dynamic timing model management approaches that efficiently improve both accuracy and speed of such models via automatically calibrating the simulation granularity. The contributions of this dissertation are twofold: We first establish an infrastructure for efficient host-compiled multi-core platform simulation by developing (a) abstract models of both real-time OSs and processors that replicate timing-accurate hardware/software interactions and enable full-system co-simulation, and (b) quantitative and analytical studies of host-compiled simulation principles to analyze error bounds and investigate possible improvements. Building on this infrastructure, we further propose specific techniques for improving accuracy and speed tradeoffs in host-compiled simulation by developing (c) an automatic timing granularity adjustment technique based on dynamically observing system state to control the simulation, (d) an out-of-order cache hierarchy modeling approach to efficiently reorder memory access behavior in the presence of temporal decoupling, and (e) a synchronized timing model to align platform threads to run efficiently in parallel simulation. Results as applied to industrial-strength platforms confirm that by providing careful abstractions and dynamic timing management, our models can achieve full-system simulations at equivalent speeds of more than a thousand MIPS with less than 3% timing error. Coupled with the capability to easily adjust simulation parameters and configurations, this demonstrates the benefits of our platform models for early application development and exploration.Electrical and Computer Engineerin

Explorations of the viability of ARM and Xeon Phi for physics processing

We report on our investigations into the viability of the ARM processor and the Intel Xeon Phi co-processor for scientific computing. We describe our experience porting software to these processors and running benchmarks using real physics applications to explore the potential of these processors for production physics processing.Comment: Submitted to proceedings of the 20th International Conference on Computing in High Energy and Nuclear Physics (CHEP13), Amsterda

The use of field-programmable gate arrays for the hardware acceleration of design automation tasks

This paper investigates the possibility of using Field-Programmable Gate Arrays (Fr’GAS) as reconfigurable co-processors for workstations to produce moderate speedups for most tasks in the design process, resulting in a worthwhile overall design process speedup at low cost and allowing algorithm upgrades with no hardware modification. The use of FPGAS as hardware accelerators is reviewed and then achievable speedups are predicted for logic simulation and VLSI design rule checking tasks for various FPGA co-processor arrangements

QCDGPU: open-source package for Monte Carlo lattice simulations on OpenCL-compatible multi-GPU systems

The multi-GPU open-source package QCDGPU for lattice Monte Carlo simulations of pure SU(N) gluodynamics in external magnetic field at finite temperature and O(N) model is developed. The code is implemented in OpenCL, tested on AMD and NVIDIA GPUs, AMD and Intel CPUs and may run on other OpenCL-compatible devices. The package contains minimal external library dependencies and is OS platform-independent. It is optimized for heterogeneous computing due to the possibility of dividing the lattice into non-equivalent parts to hide the difference in performances of the devices used. QCDGPU has client-server part for distributed simulations. The package is designed to produce lattice gauge configurations as well as to analyze previously generated ones. QCDGPU may be executed in fault-tolerant mode. Monte Carlo procedure core is based on PRNGCL library for pseudo-random numbers generation on OpenCL-compatible devices, which contains several most popular pseudo-random number generators.Comment: Presented at the Third International Conference "High Performance Computing" (HPC-UA 2013), Kyiv, Ukraine; 9 pages, 2 figure

NeuroFlow: A General Purpose Spiking Neural Network Simulation Platform using Customizable Processors

© 2016 Cheung, Schultz and Luk.NeuroFlow is a scalable spiking neural network simulation platform for off-the-shelf high performance computing systems using customizable hardware processors such as Field-Programmable Gate Arrays (FPGAs). Unlike multi-core processors and application-specific integrated circuits, the processor architecture of NeuroFlow can be redesigned and reconfigured to suit a particular simulation to deliver optimized performance, such as the degree of parallelism to employ. The compilation process supports using PyNN, a simulator-independent neural network description language, to configure the processor. NeuroFlow supports a number of commonly used current or conductance based neuronal models such as integrate-and-fire and Izhikevich models, and the spike-timing-dependent plasticity (STDP) rule for learning. A 6-FPGA system can simulate a network of up to ~600,000 neurons and can achieve a real-time performance of 400,000 neurons. Using one FPGA, NeuroFlow delivers a speedup of up to 33.6 times the speed of an 8-core processor, or 2.83 times the speed of GPU-based platforms. With high flexibility and throughput, NeuroFlow provides a viable environment for large-scale neural network simulation