SOCRATES — A seamless online compiler and system runtime autotuning framework for energy-aware applications

Gadioli, Davide; Nobre, Ricardo; Pinto, Pedro; Vitali, Emanuele; Ashouri, Amir H.; Palermo, Gianluca; Cardoso, João M. P.; Silvano, Cristina

doi:10.23919/date.2018.8342183

Cited by 14 publications

(11 citation statements)

References 12 publications

(9 reference statements)

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“…Fig. 10 shows a fragment of a simplified version of such a strategy (used in Gadioli et al [37]), which optimizes each version differently by choosing different compilation flags. In other instances, we also change the code of each version, e.g., through the application of different loop transformations.…”

Section: Multiversioningmentioning

confidence: 99%

“…This kind of strategy can be extended with other variables to create more complex applications with more potential for performance optimization. For instance, in Gadioli et al [37], we targeted kernels with OpenMP pragmas, and we added another dimension to multiversioning by also considering two possible values for the proc_bind clause. In the end, we exposed three knobs: the number of threads, compiler optimization flags, and the proc_bind value.…”

Section: Multiversioningmentioning

confidence: 99%

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Pegasus: Performance Engineering for Software Applications Targeting HPC Systems

Pinto

Bispo

Cardoso

et al. 2022

IIEEE Trans. Software Eng.

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Developing and optimizing software applications for high performance and energy efficiency is a very challenging task, even when considering a single target machine. For instance, optimizing for multicore-based computing systems requires in-depth knowledge about programming languages, application programming interfaces (APIs), compilers, performance tuning tools, and computer architecture and organization. Many of the tasks of performance engineering methodologies require manual efforts and the use of different tools not always part of an integrated toolchain. This paper presents Pegasus, a performance engineering approach supported by a framework that consists of a source-to-source compiler, controlled and guided by strategies programmed in a Domain-Specific Language, and an autotuner. Pegasus is a holistic and versatile approach spanning various decision layers composing the software stack, and exploiting the system capabilities and workloads effectively through the use of runtime autotuning. The Pegasus approach helps developers by automating tasks regarding the efficient implementation of software applications in multicore computing systems. These tasks focus on application analysis, profiling, code transformations, and the integration of runtime autotuning. Pegasus allows developers to program their strategies or to automatically apply existing strategies to software applications in order to ensure the compliance of non-functional requirements, such as performance and energy efficiency. We show how to apply Pegasus and demonstrate its applicability and effectiveness in a complex case study, which includes tasks from a smart navigation system. !• Analysis and Profiling: Incremental analysis and profiling of the software application and impact of NFRs. This analysis can rely on static and dynamic information and may involve "what-if" analysis and design-space exploration (DSE).• Strategy Selection and Development: Selection of strategies to target NFRs. With the knowledge acquired by the analysis, developers can decide to apply strategies from a catalog (e.g., loop transfor-

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Section: Multiversioningmentioning

confidence: 99%

Section: Multiversioningmentioning

confidence: 99%

Pegasus: Performance Engineering for Software Applications Targeting HPC Systems

Pinto

Bispo

Cardoso

et al. 2022

IIEEE Trans. Software Eng.

Self Cite

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“…Online tuning is easier to implement than dynamic tuning (there is no runtime compilation), but it is not practical when the number of possible code variants is high. An examples of an online tuner is SOCRATES [17,33].…”

Section: Related Workmentioning

confidence: 99%

A benchmark set of highly-efficient CUDA and OpenCL kernels and its dynamic autotuning with Kernel Tuning Toolkit

Petrovič

Střelák

Hozzová

et al. 2020

Future Generation Computer Systems

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In recent years, the heterogeneity of both commodity and supercomputers hardware has increased sharply. Accelerators, such as GPUs or Intel Xeon Phi co-processors, are often key to improving speed and energy efficiency of highly-parallel codes. However, due to the complexity of heterogeneous architectures, optimization of codes for a certain type of architecture as well as porting codes across different architectures, while maintaining a comparable level of performance, can be extremely challenging. Addressing the challenges associated with performance optimization and performance portability, autotuning has gained a lot of interest. Autotuning of performance-relevant source-code parameters allows to automatically tune applications without hard coding optimizations and thus helps with keeping the performance portable. In this paper, we introduce a benchmark set of ten autotunable kernels for important computational problems implemented in OpenCL or CUDA. Using our Kernel Tuning Toolkit, we show that with autotuning most of the kernels reach near-peak performance on various GPUs and outperform baseline implementations on CPUs and Xeon Phis. Our evaluation also demonstrates that autotuning is key to performance portability. In addition to offline tuning, we also introduce dynamic autotuning of code optimization parameters during application runtime. With dynamic tuning, the Kernel Tuning Toolkit enables applications to re-tune performance-critical kernels at runtime whenever needed, for example, when input data changes. Although it is generally believed that autotuning spaces tend to be too large to be searched during application runtime, we show that it is not necessarily the case when tuning spaces are designed rationally. Many of our kernels reach near peak-performance with moderately sized tuning spaces that can be searched at runtime with acceptable overhead. Finally we demonstrate, how dynamic performance tuning can be integrated into a real-world application from cryo-electron microscopy domain.

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“…To hide the complexity of the application enhancement, we use LARA aspects for configuring mARGOt and for instrumenting the code with related API. Figure 10 provides a simple example of a LARA aspect where mARGOt has been configured (lines 5-20) to actuate on a software knob Knob1 and target error and throughput metrics [25]. In particular, the optimization problem has been defined as the maximization of the throughput while keeping the error under a certain threshold.…”

Section: Self-adaptivity and Autotuningmentioning

confidence: 99%

The ANTAREX domain specific language for high performance computing

Silvano

Agosta

Bartolini

et al. 2019

Microprocessors and Microsystems

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The ANTAREX project relies on a Domain Specific Language (DSL) based on Aspect Oriented Programming (AOP) concepts to allow applications to enforce extra functional properties such as energy-efficiency and performance and to optimize Quality of Service (QoS) in an adaptive way. The DSL approach allows the definition of energyefficiency, performance, and adaptivity strategies as well as their enforcement at runtime through application autotuning and resource and power management. In this paper, we present an overview of the key outcome of the project, the ANTAREX DSL, and some of its capabilities through a number of examples, including how the DSL is applied in the context of the project use cases.

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SOCRATES — A seamless online compiler and system runtime autotuning framework for energy-aware applications

Cited by 14 publications

References 12 publications

Pegasus: Performance Engineering for Software Applications Targeting HPC Systems

Pegasus: Performance Engineering for Software Applications Targeting HPC Systems

A benchmark set of highly-efficient CUDA and OpenCL kernels and its dynamic autotuning with Kernel Tuning Toolkit

The ANTAREX domain specific language for high performance computing

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