On software design for stochastic processors

Sloan, Joseph; Sartori, John; Kumar, Rakesh

doi:10.1145/2228360.2228524

Cited by 13 publications

(12 citation statements)

References 18 publications

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“…The API includes calibration functions that build application-specific QoS models for the outputs of the approximated blocks of code, as well as re-calibration functions for correcting unacceptable errors that may incur due to approximation. Sloan et al [19] provide guidelines for manual control of approximate computation and error checking in software. These frameworks delegate the control of approximate code execution to the programmer.…”

Section: Other Approximation Frameworkmentioning

confidence: 99%

Exploiting Significance of Computations for Energy-Constrained Approximate Computing

Vassiliadis

Chalios

Parasyris

et al. 2016

Int J Parallel Prog

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Approximate execution is a viable technique for environments with energy constraints, provided that applications are given the mechanisms to produce outputs of the highest possible quality within the available energy budget. This paper introduces a framework for energy-constrained execution with controlled and graceful quality loss. A simple programming model allows developers to structure the computation in different tasks, and to express the relative importance of these tasks for the quality of the end result. For non-significant tasks, the developer can also supply less costly, approximate versions. The target energy consumption for a given execution is specified when the application is launched. A significance-aware runtime system employs an application-specific analytical energy model to decide how many cores to use for the execution, the operating frequency for these cores, as well as the degree of task approximation, so as to maximize the quality of the output while meeting the user-specified energy constraints.Evaluation on a dual-socket 16-core Intel platform using 9 kernels and applications shows that the proposed framework performs very close to an oracle always selecting the optimal configuration, both in terms of energy efficiency and quality of results. Also, a comparison with loop perforation (a well-known compile-time approximation technique), shows that the proposed framework results in significantly higher quality for the same energy budget.

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Section: Other Approximation Frameworkmentioning

confidence: 99%

Exploiting Significance of Computations for Energy-Constrained Approximate Computing

Vassiliadis

Chalios

Parasyris

et al. 2016

Int J Parallel Prog

View full text Add to dashboard Cite

show abstract

“…Compared to approximate programming APIs for example Green [1] and stochastic processor software APIs [15], our programming model offers the higher level abstraction of computational significance for managing quality, while delegating the selection of tasks to approximate and the control of result quality to a dynamic optimizer and the runtime system. Contrary to approximation techniques via loop perforation implemented in the compiler [13], our programming model uses domain expertise available from the programmer, which we demonstrate to be necessary for effective approximation in at least one application domain.…”

Section: Related Workmentioning

confidence: 99%

A significance-driven programming framework for energy-constrained approximate computing

Vassiliadis

Chalios

Parasyris

et al. 2015

Proceedings of the 12th ACM International Conference on Computing Frontiers

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Approximate execution is a viable technique for energy-constrained environments, provided that applications have the mechanisms to produce outputs of the highest possible quality within the given energy budget.We introduce a framework for energy-constrained execution with controlled and graceful quality loss. A simple programming model allows users to express the relative importance of computations for the quality of the end result, as well as minimum quality requirements. The significance-aware runtime system uses an application-specific analytical energy model to identify the degree of concurrency and approximation that maximizes quality while meeting user-specified energy constraints.Evaluation on a dual-socket 8-core server shows that the proposed framework predicts the optimal configuration with high accuracy, enabling energy-constrained executions that result in significantly higher quality compared to loop perforation, a compiler approximation technique.

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“…Given this limitation, an emerging field of research is in the area of systems that operate at lower voltage levels for better energy efficiency, but with a cost in accuracy. This field, where the system's correctness has been relaxed, is known as Stochastic [21], Approximate [3] or Inexact Computing [16].…”

Section: Related Workmentioning

confidence: 99%

“…Sloan et al [21] presented an interesting work where they show three approaches to address in future systems with relaxed correctness constraints such as stochastic processors: relaxing the correctness of the application, detecting and correcting errors within the application, and transform the application to better tolerate errors.…”

Section: Related Workmentioning

confidence: 99%

Getting ready for approximate computing

Trancoso

2014

Proceedings of the 11th ACM Conference on Computing Frontiers

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Processors have evolved dramatically in the last years and current multicore systems deliver very high performance. We are observing a rapid increase in the number of cores per processor thus resulting in more dense and powerful systems. Nevertheless, this evolution will meet several challenges such as power consumption, and reliability. It is expected that, in order to improve the efficiency, future processors will contain units that are able to operate at a very low power consumption with the drawback of not guaranteeing the correctness of the produced results. This model is known as Approximate Computing. One interesting approach to exploit Approximate Computing is to make applications aware of the errors and react accordingly.For this work we focus on the Decision Support System Workloads and in particular the standard TPC-H set of queries. We first define a metric that quantifies the correctness of a query result -Quality of Result (QoR). Using this metric we analyse the impact of relaxing the correctness in the DBMS on the accuracy of the query results. In order to improve the accuracy of the results we propose a dynamic adaptive technique that is implemented as a tool above the DBMS. Using heuristics, this tool spawns a number of replica query executions on different cores and combines the results as to improve the accuracy. We evaluated our technique using real TPC-H queries and data on PostgreSQL with a simple fault-injection to emulate the Approximate Computing model. The results show that for the selected scenarios, the proposed technique is able to increase the QoR with a cost in parallel resources smaller than any alternative static approach. The results are very encouraging since the QoR is within 7% of the best possible.

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On software design for stochastic processors

Cited by 13 publications

References 18 publications

Exploiting Significance of Computations for Energy-Constrained Approximate Computing

Exploiting Significance of Computations for Energy-Constrained Approximate Computing

A significance-driven programming framework for energy-constrained approximate computing

Getting ready for approximate computing

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