Towards Low-Cost, High-Accuracy Classifiers for Linear Solver Selection

Bhowmick, Sanjukta; Toth, Brice Alan; Raghavan, Padma

doi:10.1007/978-3-642-01970-8_45

Cited by 16 publications

(5 citation statements)

References 17 publications

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“…They also limit the computation time for the most expensive features as well as the total time allowed to compute features. Bhowmick, Toth, and Raghavan (2009) consider the computational complexity of calculating problem features when selecting the features to use. They show that while achieving comparable accuracy to the full set of features, the subset of features selected by their method is significantly cheaper to compute.…”

Section: Feature Selectionmentioning

confidence: 99%

Algorithm Selection for Combinatorial Search Problems: A Survey

2014

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The Algorithm Selection Problem is concerned with selecting the best algorithm to solve a given problem on a case-by-case basis. It has become especially relevant in the last decade, as researchers are increasingly investigating how to identify the most suitable existing algorithm for solving a problem instead of developing new algorithms. This survey presents an overview of this work focusing on the contributions made in the area of combinatorial search problems, where Algorithm Selection techniques have achieved significant performance improvements. We unify and organise the vast literature according to criteria that determine Algorithm Selection systems in practice. The comprehensive classification of approaches identifies and analyses the different directions from which Algorithm Selection has been approached. This paper contrasts and compares different methods for solving the problem as well as ways of using these solutions. It closes by identifying directions of current and future research.

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Section: Feature Selectionmentioning

confidence: 99%

Algorithm Selection for Combinatorial Search Problems: A Survey

2014

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“…For this experiment, we use 6 linear solver, preconditioner combinations from the CULA Sparse toolkit [31], which is a GPU library for solving large sparse linear systems. Features used for this benchmark are based on the work by Bhowmick et al [5]. We use symmetric sparse matrices from [15] to represent sparse linear systems.…”

Section: Breadth-first Search (Bfs) Bfs Is Used As a Basis Formentioning

confidence: 99%

Architecture-Adaptive Code Variant Tuning

Muralidharan

Roy

Hall

et al. 2016

SIGOPS Oper. Syst. Rev.

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Code variants represent alternative implementations of a computation, and are common in high-performance libraries and applications to facilitate selecting the most appropriate implementation for a specific execution context (target architecture and input dataset). Automating code variant selection typically relies on machine learning to construct a model during an offline learning phase that can be quickly queried at runtime once the execution context is known. In this paper, we define a new approach called architectureadaptive code variant tuning, where the variant selection model is learned on a set of source architectures, and then used to predict variants on a new target architecture without having to repeat the training process. We pose this as a multi-task learning problem, where each source architecture corresponds to a task; we use device features in the construction of the variant selection model. This work explores the effectiveness of multi-task learning and the impact of different strategies for device feature selection. We evaluate our approach on a set of benchmarks and a collection of six NVIDIA GPU architectures from three distinct generations. We achieve performance results that are mostly comparable to the previous approach of tuning for a single GPU architecture without having to repeat the learning phase.

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“…For this experiment, we use 6 (linear solver, preconditioner) combinations from the CULA Sparse toolkit [26], which is a GPU library for solving large sparse linear systems. We select features for this benchmark based on the work by Bhowmick et al [27]. These features reflect different numerical properties of sparse matrices such as trace and 1-norm.…”

Section: Benchmarksmentioning

confidence: 99%

Nitro: A Framework for Adaptive Code Variant Tuning

Muralidharan

Shantharam

Hall

et al. 2014

2014 IEEE 28th International Parallel and Distributed Processing Symposium

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Abstract-Autotuning systems intelligently navigate a search space of possible implementations of a computation to find the implementation(s) that best meets a specific optimization criteria, usually performance. This paper describes Nitro, a programmer-directed autotuning framework that facilitates tuning of code variants, or alternative implementations of the same computation. Nitro provides a library interface that permits programmers to express code variants along with metainformation that aids the system in selecting among the set of variants at run time. Machine learning is employed to build a model through training on this meta-information, so that when a new input is presented, Nitro can consult the model to select the appropriate variant. In experiments with five real-world irregular GPU benchmarks from sparse numerical methods, graph computations and sorting, Nitro-tuned variants achieve over 93% of the performance of variants selected through exhaustive search. Further, we describe optimizations and heuristics in Nitro that substantially reduce training time and other overheads.

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Towards Low-Cost, High-Accuracy Classifiers for Linear Solver Selection

Cited by 16 publications

References 17 publications

Algorithm Selection for Combinatorial Search Problems: A Survey

Algorithm Selection for Combinatorial Search Problems: A Survey

Architecture-Adaptive Code Variant Tuning

Nitro: A Framework for Adaptive Code Variant Tuning

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