Design space exploration in heterogeneous platforms using OpenMP

In the fields of high performance computing (HPC) and embedded systems, the current trend is to employ heterogeneous platforms which integrate general purpose CPUs with specialized accelerators such as GPUs and FPGAs. Programming these architectures to approach their theoretical performance limits is a complex issue. In this article, we present a design methodology targeting heterogeneous platforms which combines a novel dynamic offloading mechanism for OpenMP and a scheduling strategy for assigning tasks to accelerator devices. The current OpenMP offloading model depends on the compiler supporting each target device, with many architectures still unsupported by the most popular compilers, such as GCC and Clang. In our approach, the software and/or hardware design flows for programming the accelerators are dissociated from the host OpenMP compiler and the device-specific implementations are dynamically loaded at runtime. Moreover, the assignment of tasks to computing resources is dynamically evaluated at runtime, with the aim of maximizing performance when using the available resources. The proposed methodology has been applied to a video processing system as a test case. The results demonstrate the flexibility of the proposal by exploiting different heterogeneous platforms and design particularities of devices, leading to a significant performance improvement.This work has been funded by FEDER/Ministerio de Ciencia, Innovación y Universidades – Agencia Estatal de Investigacion/TEC2017-86722-C4-3-R, also under the FitOptiVis Project (ECSEL2017-1-737451), which is funded by the EU (H2020) and Ministerio de Ciencia, Innovación y Universidades

A General Hardware/Software Co-design Methodology for Embedded Signal Processing and Multimedia Workloads

This paper presents a hardware/software co-design methodology for partitioning real-time embedded multimedia applications between software programmable DSPs and hardware based FPGA coprocessors. By following a strict set of guidelines, the input application is partitioned between software executing on a programmable DSP and hardware based FPGA implementation to alleviate computational bottlenecks in modern VLIW style DSP architectures used in embedded systems. This methodology is applied to channel estimation firmware in 3.5G wireless receivers, as well as software based H.263 video decoders. As much as an 11x improvement in runtime performance can be achieved by partitioning performance critical software kernels in these workloads into a hardware based FPGA implementation executing in tandem with the existing host DSP.Nokia Inc.Texas InstrumentsNational Science Foundatio

Optimización del rendimiento y la eficiencia energética en sistemas masivamente paralelos

RESUMEN Los sistemas heterogéneos son cada vez más relevantes, debido a sus capacidades de rendimiento y eficiencia energética, estando presentes en todo tipo de plataformas de cómputo, desde dispositivos embebidos y servidores, hasta nodos HPC de grandes centros de datos. Su complejidad hace que sean habitualmente usados bajo el paradigma de tareas y el modelo de programación host-device. Esto penaliza fuertemente el aprovechamiento de los aceleradores y el consumo energético del sistema, además de dificultar la adaptación de las aplicaciones. La co-ejecución permite que todos los dispositivos cooperen para computar el mismo problema, consumiendo menos tiempo y energía. No obstante, los programadores deben encargarse de toda la gestión de los dispositivos, la distribución de la carga y la portabilidad del código entre sistemas, complicando notablemente su programación. Esta tesis ofrece contribuciones para mejorar el rendimiento y la eficiencia energética en estos sistemas masivamente paralelos. Se realizan propuestas que abordan objetivos generalmente contrapuestos: se mejora la usabilidad y la programabilidad, a la vez que se garantiza una mayor abstracción y extensibilidad del sistema, y al mismo tiempo se aumenta el rendimiento, la escalabilidad y la eficiencia energética. Para ello, se proponen dos motores de ejecución con enfoques completamente distintos. EngineCL, centrado en OpenCL y con una API de alto nivel, favorece la máxima compatibilidad entre todo tipo de dispositivos y proporciona un sistema modular extensible. Su versatilidad permite adaptarlo a entornos para los que no fue concebido, como aplicaciones con ejecuciones restringidas por tiempo o simuladores HPC de dinámica molecular, como el utilizado en un centro de investigación internacional. Considerando las tendencias industriales y enfatizando la aplicabilidad profesional, CoexecutorRuntime proporciona un sistema flexible centrado en C++/SYCL que dota de soporte a la co-ejecución a la tecnología oneAPI. Este runtime acerca a los programadores al dominio del problema, posibilitando la explotación de estrategias dinámicas adaptativas que mejoran la eficiencia en todo tipo de aplicaciones.ABSTRACT Heterogeneous systems are becoming increasingly relevant, due to their performance and energy efficiency capabilities, being present in all types of computing platforms, from embedded devices and servers to HPC nodes in large data centers. Their complexity implies that they are usually used under the task paradigm and the host-device programming model. This strongly penalizes accelerator utilization and system energy consumption, as well as making it difficult to adapt applications. Co-execution allows all devices to simultaneously compute the same problem, cooperating to consume less time and energy. However, programmers must handle all device management, workload distribution and code portability between systems, significantly complicating their programming. This thesis offers contributions to improve performance and energy efficiency in these massively parallel systems. The proposals address the following generally conflicting objectives: usability and programmability are improved, while ensuring enhanced system abstraction and extensibility, and at the same time performance, scalability and energy efficiency are increased. To achieve this, two runtime systems with completely different approaches are proposed. EngineCL, focused on OpenCL and with a high-level API, provides an extensible modular system and favors maximum compatibility between all types of devices. Its versatility allows it to be adapted to environments for which it was not originally designed, including applications with time-constrained executions or molecular dynamics HPC simulators, such as the one used in an international research center. Considering industrial trends and emphasizing professional applicability, CoexecutorRuntime provides a flexible C++/SYCL-based system that provides co-execution support for oneAPI technology. This runtime brings programmers closer to the problem domain, enabling the exploitation of dynamic adaptive strategies that improve efficiency in all types of applications.Funding: This PhD has been supported by the Spanish Ministry of Education (FPU16/03299 grant), the Spanish Science and Technology Commission under contracts TIN2016-76635-C2-2-R and PID2019-105660RB-C22. This work has also been partially supported by the Mont-Blanc 3: European Scalable and Power Efficient HPC Platform based on Low-Power Embedded Technology project (G.A. No. 671697) from the European Union’s Horizon 2020 Research and Innovation Programme (H2020 Programme). Some activities have also been funded by the Spanish Science and Technology Commission under contract TIN2016-81840-REDT (CAPAP-H6 network). The Integration II: Hybrid programming models of Chapter 4 has been partially performed under the Project HPC-EUROPA3 (INFRAIA-2016-1-730897), with the support of the EC Research Innovation Action under the H2020 Programme. In particular, the author gratefully acknowledges the support of the SPMT Department of the High Performance Computing Center Stuttgart (HLRS)

HAL-ASOS accelerator model: evolutive elasticity by design

To address the integration of software threads and hardware accelerators into the Linux Operating System (OS) programming models, an accelerator architecture is proposed, based on micro-programmable hardware system calls, which fully export these resources into the Linux OS user-space through a design-specific virtual file system. The proposed HAL-ASOS accelerator model is split into a user-defined Hardware Task and a parameterizable Hardware Kernel with three differentiated transfer channels, aiming to explore distinct BUS technology interfaces and promote the accelerator to a first-class computing unit. This paper focuses on the Hardware Kernel and mainly its microcode control unit, which will leverage the elasticity to naturally evolve with Linux OS through key differentiating capabilities of field programmable gate arrays (FPGAs) when compared to the state of the art. To comply with the evolutive nature of Linux OS, or any Hardware Task incremental features, the proposed model generates page-faults signaling runtime errors that are handled at the kernel level as part of the virtual file system runtime. To evaluate the accelerator model’s programmability and its performance, a client-side application based on the AES 128-bit algorithm was implemented. Experiments demonstrate a flexible design approach in terms of hardware and software reconfiguration and significant performance increases consistent with rising processing demands or clock design frequencies.This work has been supported by FCT-Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020

Exploring heterogeneous scheduling for edge computing with CPU and FPGA MPSoCs

This paper presents a framework targeted to low-cost and low-power heterogeneous MultiProcessors that exploits FPGAs and multicore CPUs, with the overarching goal of providing developers with a productive programming model and runtime support to fully use all the processing resources available. FPGA productivity is achieved using a high-level programming model based on OpenCL, the standard for cross-platform parallel heterogeneous programming. In this work, we focus on the parallel for pattern, and as part of the runtime support for this pattern, we leverage a new scheduler that strives to maximize the number of iterations per joule by dynamically and adaptively partitioning the iteration space between the multicore and the accelerator when working simultaneously. A total of 7 benchmarks are ported and optimized for a low-cost DE1 board. The results show that the heterogeneous solution can improve performance up to 2.9x and increases energy efficiency up to 2.7x compared tothe traditional approach of keeping all the CPU cores idle while the accelerator computes the workload. Our results also demonstrate two interesting insights: First, an adaptive scheduler able to find at runtime the right chunk size for each type of application and device configuration is an essential component for these kinds of heterogeneous platforms, and second, device configurations that provide higher throughput do not always achieve better energy eciency when only the running power (excluding the idle power component) is considered

Automatic Nested Loop Acceleration on FPGAs Using Soft CGRA Overlay

Session 1: HLS Toolingpostprin

Lightweight asynchronous scheduling in heterogeneous reconfigurable systems

The trend for heterogeneous embedded systems is the integration of accelerators and general-purpose CPU cores on the same die. In these integrated architectures, like the Zynq UltraScale+ board (CPU+FPGA) that we target in this work, hardware support for shared memory and low-overhead synchronization between the accelerator and the CPU cores make the case for exploring strategies that exploit a tight collaboration between the CPUs and the accelerator. In this paper we propose a novel lightweight scheduling strategy, FastFit, targeted to FPGA accelerators, and a new scheduler based on it, named MultiFastFit, which asynchronously tackles heterogeneous systems comprised of a variety of CPU cores and FPGA IPs. Our strategy significantly reduces the overhead to automatically compute the near-optimal chunksizes when compared to a previous state-of-the-art auto-tuned approach, which makes our approach more suitable for fine-grained applications. Additionally, our scheduler MultiFastFit has been designed to enable the efficient co-execution of work among compute devices in such a way that all the devices are busy while minimizing the load unbalance. Our approaches have been evaluated using four benchmarks carefully tuned for the low-power UltraScale+ platform. Our experiments demonstrate that the FastFit strategy always finds the near-optimal FPGA chunksize for any device configuration at a reasonable cost, even for fine-grained and irregular applications, and that heterogeneous CPU+FPGA co-executions that exploit all the compute devices are usually faster and more energy efficient than the CPU-only and FPGA-only executions. We have also compared MultiFastFit with other state-of-the-art scheduling strategies, finding that it outperforms other auto-tuned approach up to 2x and it achieves similar results to manually-tuned schedulers without requiring an offline search of the ideal CPU-FPGA partition or FPGA chunk granularity. © 2022 The Author