MineBench: A Benchmark Suite for Data Mining Workloads

Narayanan, Ramanathan; Ozisikyilmaz, Berkin; Zambreno, Joseph; Memik, Gokhan; Choudhary, Alok

doi:10.1109/iiswc.2006.302743

Cited by 194 publications

(97 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In our study, we analyze applications belonging to four major domains: clustering, association rule mining, classification, and sequence mining. We have selected five applications from NU-MineBench, a data mining applications benchmark suite [7]. In our application selection, we have given priority to the applications that have the most floating point operations, since these are negatively affected while executing on embedded environments.…”

Section: Data Mining Applicationsmentioning

confidence: 99%

“…We also compute the fraction of floating point operations within the executed instructions [8], and surmise that there is significant scope for optimization by converting the floating point operations to fixed point arithmetic. Detailed information about the K-Means, Fuzzy K-Means, Utility, ScalParC and PLSA applications and their corresponding datasets can be found in [7].…”

Section: Data Mining Applicationsmentioning

confidence: 99%

See 1 more Smart Citation

Quantization Error and Accuracy-Performance Tradeoffs for Embedded Data Mining Workloads

Narayanan

Özışkyılmaz

Memik

et al. 2007

Computational Science – ICCS 2007

Self Cite

View full text Add to dashboard Cite

Abstract. Data mining is the process of automatically finding implicit, previously unknown and potentially useful information from large volumes of data. Embedded systems are increasingly used for sophisticated data mining algorithms to make intelligent decisions while storing and analyzing data. Since data mining applications are designed and implemented considering the resources available on a conventional computing platform, their performance degrades when executed on an embedded system. In this paper, we analyze the bottlenecks faced in implementing these algorithms in an embedded environment and explore their portability to the embedded systems domain. Particularly, we analyze the floating point computation in these applications and convert them into fixed point operations. Our results reveal that the execution time of five representative applications can be reduced by as much as 11.5× and 5.2× on average, without a significant impact on accuracy.

show abstract

Section: Data Mining Applicationsmentioning

confidence: 99%

Section: Data Mining Applicationsmentioning

confidence: 99%

Quantization Error and Accuracy-Performance Tradeoffs for Embedded Data Mining Workloads

Narayanan

Özışkyılmaz

Memik

et al. 2007

Computational Science – ICCS 2007

Self Cite

View full text Add to dashboard Cite

show abstract

“…We examine six parallel applications from the MineBench [12] data mining suite. 1 These applications are written in C/C++ and parallelized using OpenMP.…”

Section: A Workloadmentioning

confidence: 99%

“…In order to study these applications, Narayanan et al have provided a data mining application suite called MineBench [12]. MineBench contains fifteen applications, twelve of which are parallel, that implement algorithms for clustering, classification, association rule mining, optimization, and structured learning.…”

Section: A Data Mining Characteristicsmentioning

confidence: 99%

“…This paper analyzes parallel applications from the MineBench data mining suite [12] to determine whether they exhibit characteristics exploited by a set of approaches developed for improving the performance of applications on chip multiprocessors. Our architecture independent examination of data usage enables us to observe the type, frequency, and predictability of data accesses and data sharing.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Understanding the applicability of CMP performance optimizations on data mining applications

Jibaja

Shaw

2009

2009 IEEE International Symposium on Workload Characterization (IISWC)

View full text Add to dashboard Cite

Abstract-A major challenge to the creation of chip multiprocessors is designing the on-chip memory and communication resources to efficiently support parallel workloads. A variety of cache organizations, data management techniques, and hardware optimizations that take advantage of specific data characteristics have been developed to improve application performance. The success of these approaches depends on applications exhibiting the presumed data characteristics.Data mining applications are a growing class of applications that discover patterns in large sets of collected data. Because these applications tend to be highly parallelizable, they represent an important workload for chip multiprocessors. However, the memory intensive nature of these applications means that they will stress these chips' memory and communication resources.In this paper, we examine the data usage characteristics of a set of parallel data mining applications to determine the applicability of existing chip multiprocessor approaches to these applications. We show diversity of characteristics across and within these applications, making some techniques more applicable than others. We also discuss software approaches that could be used to either provide information to the hardware or assist the hardware in dynamically discovering data characteristics needed for the deployment of these techniques.

show abstract

STMs in practice: Partial rollback vs pure abort mechanisms

Anand

Shyamasundar

Peri

2018

Concurrency and Computation

View full text Add to dashboard Cite

Summary In this paper, we propose an enhanced Automatic Checkpointing and Partial Rollback (CaPR++) algorithm to realize Software Transactional Memory (STM), that employs partial rollback mechanism for conflict resolution. We have comparatively evaluated the “Abort” and “Partial Rollback” mechanisms for STMs. For purposes of comparison, we have used the state‐of‐the‐art RSTM system and for the “Partial Rollback”, and we have used our earlier CaPR+ algorithm that has been enhanced for our requirements. Note that we have enriched the STAMP benchmarks with varied delayed transaction times. The results obtained demonstrate the effectiveness of the Partial Rollback mechanism over pure abort mechanisms for applications consisting of large transaction delays, with up to 1.6x performance gain for applications with large transactional delays. Our study makes the case for a hybrid system of pure aborts and partial rollbacks, which can extract the benefits of both mechanisms. Keeping in line with our study, we have proposed a hybrid implementation where some of the transactions of an application subscribe to abort mechanisms and the rest to partial rollback. Our initial implementation demonstrates various scenarios where the hybrid approach outperforms the pure abort and partial rollback approaches.

show abstract

MineBench: A Benchmark Suite for Data Mining Workloads

Abstract: Abstract

Cited by 194 publications

References 15 publications

Quantization Error and Accuracy-Performance Tradeoffs for Embedded Data Mining Workloads

Quantization Error and Accuracy-Performance Tradeoffs for Embedded Data Mining Workloads

Understanding the applicability of CMP performance optimizations on data mining applications

STMs in practice: Partial rollback vs pure abort mechanisms

Contact Info

Product

Resources

About