271 research outputs found
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)
Design and Deployment of Photo2Building: A Cloud-based Procedural Modeling Tool as a Service
We present a Photo2Building tool to create a plausible 3D model of a building
from only a single photograph. Our tool is based on a prior desktop version
which, as described in this paper, is converted into a client-server model,
with job queuing, web-page support, and support of concurrent usage. The
reported cloud-based web-accessible tool can reconstruct a building in 40
seconds on average and costing only 0.60 USD with current pricing. This
provides for an extremely scalable and possibly widespread tool for creating
building models for use in urban design and planning applications. With the
growing impact of rapid urbanization on weather and climate and resource
availability, access to such a service is expected to help a wide variety of
users such as city planners, urban meteorologists worldwide in the quest to
improved prediction of urban weather and designing climate-resilient cities of
the future.Comment: 7 pages, 7 figures, PEARC '20: Practice and Experience in Advanced
Research Computing, July 26--30, 2020, Portland, OR, US
Indexed dependence metadata and its applications in software performance optimisation
To achieve continued performance improvements, modern microprocessor design is tending to concentrate
an increasing proportion of hardware on computation units with less automatic management
of data movement and extraction of parallelism. As a result, architectures increasingly include multiple
computation cores and complicated, software-managed memory hierarchies. Compilers have
difficulty characterizing the behaviour of a kernel in a general enough manner to enable automatic
generation of efficient code in any but the most straightforward of cases.
We propose the concept of indexed dependence metadata to improve application development and
mapping onto such architectures. The metadata represent both the iteration space of a kernel and the
mapping of that iteration space from a given index to the set of data elements that iteration might
use: thus the dependence metadata is indexed by the kernel’s iteration space. This explicit mapping
allows the compiler or runtime to optimise the program more efficiently, and improves the program
structure for the developer. We argue that this form of explicit interface specification reduces the need
for premature, architecture-specific optimisation. It improves program portability, supports intercomponent
optimisation and enables generation of efficient data movement code.
We offer the following contributions: an introduction to the concept of indexed dependence metadata
as a generalisation of stream programming, a demonstration of its advantages in a component
programming system, the decoupled access/execute model for C++ programs, and how indexed dependence
metadata might be used to improve the programming model for GPU-based designs. Our
experimental results with prototype implementations show that indexed dependence metadata supports
automatic synthesis of double-buffered data movement for the Cell processor and enables aggressive
loop fusion optimisations in image processing, linear algebra and multigrid application case
studies
OpenCL Actors - Adding Data Parallelism to Actor-based Programming with CAF
The actor model of computation has been designed for a seamless support of
concurrency and distribution. However, it remains unspecific about data
parallel program flows, while available processing power of modern many core
hardware such as graphics processing units (GPUs) or coprocessors increases the
relevance of data parallelism for general-purpose computation.
In this work, we introduce OpenCL-enabled actors to the C++ Actor Framework
(CAF). This offers a high level interface for accessing any OpenCL device
without leaving the actor paradigm. The new type of actor is integrated into
the runtime environment of CAF and gives rise to transparent message passing in
distributed systems on heterogeneous hardware. Following the actor logic in
CAF, OpenCL kernels can be composed while encapsulated in C++ actors, hence
operate in a multi-stage fashion on data resident at the GPU. Developers are
thus enabled to build complex data parallel programs from primitives without
leaving the actor paradigm, nor sacrificing performance. Our evaluations on
commodity GPUs, an Nvidia TESLA, and an Intel PHI reveal the expected linear
scaling behavior when offloading larger workloads. For sub-second duties, the
efficiency of offloading was found to largely differ between devices. Moreover,
our findings indicate a negligible overhead over programming with the native
OpenCL API.Comment: 28 page
Software architectural support for tangible user interfaces in distributed, heterogeneous computing environments
This research focuses on tools that support the development of tangible interaction-based applications for distributed computing environments. Applications built with these tools are capable of utilizing heterogeneous resources for tangible interaction and can be reconfigured for different contexts with minimal code changes. Current trends in computing, especially in areas such as computational science, scientific visualization and computer supported collaborative work, foreshadow increasing complexity, distribution and remoteness of computation and data. These trends imply that tangible interface developers must address concerns of both tangible interaction design and networked distributed computing. In this dissertation, we present a software architecture that supports separation of these concerns. Additionally, a tangibles-based software development toolkit based on this architecture is presented that enables the logic of elements within a tangible user interface to be mapped to configurations that vary in the number, type and location of resources within a given tangibles-based system
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