Autotuning and Self-Adaptability in Concurrency Libraries

Autotuning is an established technique for optimizing the performance of parallel applications. However, programmers must prepare applications for autotuning, which is tedious and error prone coding work. We demonstrate how applications become ready for autotuning with few or no modifications by extending Threading Building Blocks (TBB), a library for parallel programming, with autotuning. The extended TBB library optimizes all application-independent tuning parameters fully automatically. We compare manual effort, autotuning overhead and performance gains on 17 examples. While some examples benefit only slightly, others speed up by 28% over standard TBB.Comment: Presented at 1st Workshop on Resource Awareness and Adaptivity in Multi-Core Computing (Racing 2014) (arXiv:1405.2281

Spectral turning bands for efficient Gaussian random fields generation on GPUs and accelerators

A random field (RF) is a set of correlated random variables associated with different spatial locations. RF generation algorithms are of crucial importance for many scientific areas, such as astrophysics, geostatistics, computer graphics, and many others. Current approaches commonly make use of 3D fast Fourier transform (FFT), which does not scale well for RF bigger than the available memory; they are also limited to regular rectilinear meshes. We introduce random field generation with the turning band method (RAFT), an RF generation algorithm based on the turning band method that is optimized for massively parallel hardware such as GPUs and accelerators. Our algorithm replaces the 3D FFT with a lower‐order, one‐dimensional FFT followed by a projection step and is further optimized with loop unrolling and blocking. RAFT can easily generate RF on non‐regular (non‐uniform) meshes and efficiently produce fields with mesh sizes bigger than the available device memory by using a streaming, out‐of‐core approach. Our algorithm generates RF with the correct statistical behavior and is tested on a variety of modern hardware, such as NVIDIA Tesla, AMD FirePro and Intel Phi. RAFT is faster than the traditional methods on regular meshes and has been successfully applied to two real case scenarios: planetary nebulae and cosmological simulations

Performance-Detective: Automatic Deduction of Cheap and Accurate Performance Models

The many configuration options of modern applications make it difficult for users to select a performance-optimal configuration. Performance models help users in understanding system performance and choosing a fast configuration. Existing performance modeling approaches for applications and configurable systems either require a full-factorial experiment design or a sampling design based on heuristics. This results in high costs for achieving accurate models. Furthermore, they require repeated execution of experiments to account for measurement noise. We propose Performance-Detective, a novel code analysis tool that deduces insights on the interactions of program parameters. We use the insights to derive the smallest necessary experiment design and avoiding repetitions of measurements when possible, significantly lowering the cost of performance modeling. We evaluate Performance-Detective using two case studies where we reduce the number of measurements from up to 3125 to only 25, decreasing cost to only 2.9% of the previously needed core hours, while maintaining accuracy of the resulting model with 91.5% compared to 93.8% using all 3125 measurements

OpenTuner: An Extensible Framework for Program Autotuning

Program autotuning has been shown to achieve better or more portable performance in a number of domains. However, autotuners themselves are rarely portable between projects, for a number of reasons: using a domain-informed search space representation is critical to achieving good results; search spaces can be intractably large and require advanced machine learning techniques; and the landscape of search spaces can vary greatly between different problems, sometimes requiring domain specific search techniques to explore efficiently. This paper introduces OpenTuner, a new open source framework for building domain-specific multi-objective program autotuners. OpenTuner supports fully-customizable configuration representations, an extensible technique representation to allow for domain-specific techniques, and an easy to use interface for communicating with the program to be autotuned. A key capability inside OpenTuner is the use of ensembles of disparate search techniques simultaneously; techniques that perform well will dynamically be allocated a larger proportion of tests. We demonstrate the efficacy and generality of OpenTuner by building autotuners for 6 distinct projects and 14 total benchmarks, showing speedups over prior techniques of these projects of up to 2.8x with little programmer effort.This work is partially supported by DOE award DE-SC0005288 and DOD DARPA award HR0011-10-9-0009. This research used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231

SCALO: Scalability-Aware Parallelism Orchestration for Multi-Threaded Workloads

This article contributes a solution to orchestrate concurrent application execution to increase throughput. SCALO monitors co-executing applications at runtime to evaluate their scalability

An automatic optimizer for heterogeneous devices

Versión final aceptada de: https://doi.org/10.1016/j.future.2020.01.018This version of the article: Fernández-Fabeiro, J., Andrade, D., Fraguela, B. B., & Doallo, R. (2020). 'An automaticoptimizer for heterogeneous devices' has been accepted for publication in: Future Generation Computer Systems, 106, 572–584. The Version of Record is available online at: https://doi.org/10.1016/j.future.2020.01.018 .[Abstract]: Codes written in a naive way seldom effectively exploit the computing resources, while writing optimized codes is usually a complex task that requires certain levels of expertise. This problem is further increased in the presence of heterogeneous devices, which present more tunable parameters than regular CPUs and high sensitivity to the optimization decisions taken. Furthermore, portability is an added concern given the wide variety of accelerators available. This paper tackles this problem adding an automatic optimizer to a library that already provides an easy and portable way to program heterogeneous devices, the Heterogeneous Programming Library (HPL). Our optimizer takes as input a simple version of a code and then tunes it for the device where it is going to be executed by performing the most usual set of optimizations applicable in heterogeneous devices. These optimizations are parametrized using a set of optimization parameters that need to be tuned for the device. The HPL library has also been equipped with an autotuner that can be used to this purpose. The effectiveness of the autotuner and the optimizer has been tested on several codes and devices. The results show that the combination of the autotuner and the optimizer make the tested codes 16 times faster on average than the original codes written by the programmer.This research was supported by the Ministry of Economy and Competitiveness of Spain and FEDER funds (80%) of the EU (TIN2016-75845-P), and by the Government of Galicia (Xunta de Galicia, Spain) co-founded by the European Regional Development Fund (ERDF) under the Consolidation Programme of Competitive Reference Groups (ED431C 2017/04) as well as under Xunta de Galicia and FEDER funds of the EU (Centro de Investigación de Galicia accreditation 2019–2022, ref. ED431G2019/01)Xunta de Galicia; ED431C 2017/04Xunta de Galicia; ED431G2019/0