Predictive Runtime Code Scheduling for Heterogeneous Architectures

Jiménez, Víctor; Vilanova, Lluís; Gelado, Isaac; Gil, Marisa; Fursin, Grigori; Navarro, Nacho

doi:10.1007/978-3-540-92990-1_4

Cited by 96 publications

(67 citation statements)

References 16 publications

(16 reference statements)

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“…We also extend Milepost/cTuning technology to improve machine learning models and analyze the quality of program features to search for optimal sequences of optimization passes or polyhedral transformations [59,78]. We started combining Milepost technology with machine-learning based autoparallelization and predictive scheduling techniques [60,51,76]. We have also started investigating staged compilation techniques to balance between static and dynamic optimizations using machine learning in LLVM or Milepost GCC4CIL connected to Mono virtual machine.…”

Section: Discussionmentioning

confidence: 99%

Milepost GCC: Machine Learning Enabled Self-tuning Compiler

Fursin

Kashnikov

Memon

et al. 2011

Int J Parallel Prog

Self Cite

218

251

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Tuning compiler optimizations for rapidly evolving hardware makes porting and extending an optimizing compiler for each new platform extremely challenging. Iterative optimization is a popular approach to adapting programs to a new architecture automatically using feedback-directed compilation. However, the large number of evaluations required for each program has prevented iterative compilation from widespread take-up in production compilers. Machine learning has been proposed to tune optimizations across programs systematically but is currently limited to a few transformations, long training phases and critically lacks publicly released, stable tools.Our approach is to develop a modular, extensible, self-tuning optimization infrastructure to automatically learn the best optimizations across multiple programs and architectures based on the correlation between program features, run-time behavior and optimizations. In this paper we describe Milepost GCC, the first publicly-available open-source machine learning-based compiler. It consists of an Interactive Compilation Interface (ICI) and plugins to extract program features and exchange optimization data with the cTuning.org open public repository. It automatically adapts the internal optimization heuristic at function-level granularity to improve execution time, code size and compilation time of a new program on a given architecture. Part of the Milepost technology together with low-level ICI-inspired plugin framework is now included in the mainline GCC.We developed machine learning plugins based on probabilistic and transductive approaches to predict good combinations of optimizations. Our preliminary experimental results show that it is possible to automatically reduce the execution time of individual MiBench programs, some by more than a factor of 2, while also improving compilation 1 INRIA Saclay, France (HiPEAC member) · 2 University of Versailles Saint Quentin en Yvelines, France · 3 IBM Haifa, Israel (HiPEAC member) · 4 CAPS Entreprise, France (HiPEAC member) · 5 ARC International, UK · 6 University of Edinburgh, UK (HiPEAC member) · 2 time and code size. On average we are able to reduce the execution time of the MiBench benchmark suite by 11% for the ARC reconfigurable processor. We also present a realistic multi-objective optimization scenario for Berkeley DB library using Milepost GCC and improve execution time by approximately 17%, while reducing compilation time and code size by 12% and 7% respectively on Intel Xeon processor.

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Section: Discussionmentioning

confidence: 99%

Milepost GCC: Machine Learning Enabled Self-tuning Compiler

Fursin

Kashnikov

Memon

et al. 2011

Int J Parallel Prog

Self Cite

218

251

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“…In addition to the task block schedulers, Jiménez et al [52] propose a runtime scheduling system based on performance in past runs. Unlike CoreTSAR however the goal of their work is the scheduling of multiple applications across a set of CPU and GPU resources to lower contention and increase performance of each application instance.…”

Section: Heterogeneous Programming Modelsmentioning

confidence: 99%

Runtime Adaptation for Autonomic Heterogeneous Computing

Scogland

Feng

2014

2014 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing

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Heterogeneity is increasing across all levels of computing, with the rise of accelerators such as GPUs, FPGAs, and other coprocessors into everything from cell phones to supercomputers. More quietly it is increasing with the rise of NUMA systems, hierarchical caching, OS noise, and a myriad of other factors. As heterogeneity becomes a fact of life, efficiently managing heterogeneous compute resources is becoming a critical, and ever more complex, task. The focus of this dissertation is to lay the foundation for an autonomic system for heterogeneous computing, employing runtime adaptation to improve performance portability and performance consistency while maintaining or increasing programmability. We investigate heterogeneity arising from a myriad of factors, grouped into the dimensions of locality and capability. This work has resulted in runtime schedulers capable of automatically detecting and mitigating heterogeneity in physically homogeneous systems through MPI and adaptive coscheduling for physically heterogeneous accelerator based systems as well as a synthesis of the two to address multiple levels of heterogeneity as a coherent whole. We also discuss our current work towards the next generation of fine-grained scheduling and synchronization across heterogeneous platforms in the design of a highly-scalable and portable concurrent queue for many-core systems. Each component addresses aspects of the urgent need for automated management of the extreme and ever expanding complexity introduced by heterogeneity. I have also had the good fortune to collaborate with both Lawrence Livermore National Laboratory and Argonne National Laboratory. Experiences as an intern at each lab has had a significant effect on the final shape of my dissertation. Special thanks go to Dr. Pavan Balaji (again), Dr. Bronis de Supinski (again) and Dr. Barry Rountree. These collaborations proved to be major turning points for me, both in my research and my life as a whole.

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“…Jiménez et al [14] consider scheduling in multi-programmed heterogeneous environments. At run-time each program will be executed on all devices and the performance is collected.…”

Section: Related Workmentioning

confidence: 99%

A Static Task Partitioning Approach for Heterogeneous Systems Using OpenCL

Grewe

O’Boyle

2011

Lecture Notes in Computer Science

153

124

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Abstract. Heterogeneous multi-core platforms are increasingly prevalent due to their perceived superior performance over homogeneous systems. The best performance, however, can only be achieved if tasks are accurately mapped to the right processors. OpenCL programs can be partitioned to take advantage of all the available processors in a system. However, finding the best partitioning for any heterogeneous system is difficult and depends on the hardware and software implementation.We propose a portable partitioning scheme for OpenCL programs on heterogeneous CPU-GPU systems. We develop a purely static approach based on predictive modelling and program features. When evaluated over a suite of 47 benchmarks, our model achieves a speedup of 1.57 over a state-of-the-art dynamic run-time approach, a speedup of 3.02 over a purely multi-core approach and 1.55 over the performance achieved by using just the GPU.

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Predictive Runtime Code Scheduling for Heterogeneous Architectures

Cited by 96 publications

References 16 publications

Milepost GCC: Machine Learning Enabled Self-tuning Compiler

Milepost GCC: Machine Learning Enabled Self-tuning Compiler

Runtime Adaptation for Autonomic Heterogeneous Computing

A Static Task Partitioning Approach for Heterogeneous Systems Using OpenCL

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