4 research outputs found
A Fast Causal Profiler for Task Parallel Programs
This paper proposes TASKPROF, a profiler that identifies parallelism
bottlenecks in task parallel programs. It leverages the structure of a task
parallel execution to perform fine-grained attribution of work to various parts
of the program. TASKPROF's use of hardware performance counters to perform
fine-grained measurements minimizes perturbation. TASKPROF's profile execution
runs in parallel using multi-cores. TASKPROF's causal profile enables users to
estimate improvements in parallelism when a region of code is optimized even
when concrete optimizations are not yet known. We have used TASKPROF to isolate
parallelism bottlenecks in twenty three applications that use the Intel
Threading Building Blocks library. We have designed parallelization techniques
in five applications to in- crease parallelism by an order of magnitude using
TASKPROF. Our user study indicates that developers are able to isolate
performance bottlenecks with ease using TASKPROF.Comment: 11 page
GAPP: A Fast Profiler for Detecting Serialization Bottlenecks in Parallel Linux Applications
We present a parallel profiling tool, GAPP, that identifies serialization
bottlenecks in parallel Linux applications arising from load imbalance or
contention for shared resources . It works by tracing kernel context switch
events using kernel probes managed by the extended Berkeley Packet Filter
(eBPF) framework. The overhead is thus extremely low (an average 4% run time
overhead for the applications explored), the tool requires no program
instrumentation and works for a variety of serialization bottlenecks. We
evaluate GAPP using the Parsec3.0 benchmark suite and two large open-source
projects: MySQL and Nektar++ (a spectral/hp element framework). We show that
GAPP is able to reveal a wide range of bottleneck-related performance issues,
for example arising from synchronization primitives, busy-wait loops, memory
operations, thread imbalance and resource contention.Comment: 8 page
Optimizaci贸n del rendimiento y la eficiencia energ茅tica en sistemas masivamente paralelos
RESUMEN Los sistemas heterog茅neos son cada vez m谩s relevantes, debido a sus capacidades de rendimiento y eficiencia energ茅tica, estando presentes en todo tipo de plataformas de c贸mputo, 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 programaci贸n host-device. Esto penaliza fuertemente el aprovechamiento de los aceleradores y el consumo energ茅tico del sistema, adem谩s de dificultar la adaptaci贸n de las aplicaciones.
La co-ejecuci贸n permite que todos los dispositivos cooperen para computar el mismo problema, consumiendo menos tiempo y energ铆a. No obstante, los programadores deben encargarse de toda la gesti贸n de los dispositivos, la distribuci贸n de la carga y la portabilidad del c贸digo entre sistemas, complicando notablemente su programaci贸n.
Esta tesis ofrece contribuciones para mejorar el rendimiento y la eficiencia energ茅tica 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 abstracci贸n y extensibilidad del sistema, y al mismo tiempo se aumenta el rendimiento, la escalabilidad y la eficiencia energ茅tica. Para ello, se proponen dos motores de ejecuci贸n con enfoques completamente distintos.
EngineCL, centrado en OpenCL y con una API de alto nivel, favorece la m谩xima 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 din谩mica molecular, como el utilizado en un centro de investigaci贸n 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-ejecuci贸n a la tecnolog铆a oneAPI. Este runtime acerca a los programadores al dominio del problema, posibilitando la explotaci贸n de estrategias din谩micas 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鈥檚 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)