Diagnosing Performance Bottlenecks in Massive Data Parallel Programs
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- Type
- article
- Published
- 2016-05-16
- Cited by
- 7
- References
- 15
- OpenAlex
- https://openalex.org/W2505994417
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:8310121
Keywords
Computer science, Variety (cybernetics), SPARK (programming language), Identification (biology), Degradation (telecommunications)
References
- Broom: Sweeping Out Garbage Collection from Big Data Systems
- Trash Day: Coordinating Garbage Collection in Distributed Systems
- Reoptimizing Data Parallel Computing
- Making Sense of Performance in Data Analytics Frameworks
- Starfish: A Self-tuning System for Big Data Analytics
- New kid on the block: exploring the google+ social graph
- Optimus: a dynamic rewriting framework for data-parallel execution plans
- Mining frequent patterns without candidate generation
- SCOPE: easy and efficient parallel processing of massive data sets
- Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing
- FACADE: A Compiler and Runtime for (Almost) Object-Bounded Big Data Applications
- MapReduce: simplified data processing on large clusters
- FACADE
- Asynchronous and Anticipatory Filter-Stream Based Parallel Algorithm for Frequent Itemset Mining
Cited by
- Dynamic Reconfiguration of Data Parallel Programs
- A Framework for Analyzing Fog-Cloud Computing Cooperation Applied to Information Processing of UAVs
- Experimental Performance Analysis of Graph Analytics Frameworks
- Decision-Making for Placing Unmanned Aerial Vehicles to Implementation of Analyzing Cloud Computing Cooperation Applied to Information Processing
- Emerging UAV technology for disaster detection, mitigation, response, and preparedness
- An Edge-Fog Architecture for Distributed 3D Reconstruction and Remote Monitoring of a Power Plant Site in the Context of 5G
- RE-VETORIZAÇÃO DE CHAMADAS DE FUNÇÃO
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