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Red Fox

Haicheng Wu, Gregory Diamos, Tim Sheard, Molham Aref, Sean Baxter, Michael Garland, Sudhakar Yalamanchili
2014 Proceedings of Annual IEEE/ACM International Symposium on Code Generation and Optimization  
This paper introduces the design, implementation, and evaluation of Red Fox, a compiler and runtime infrastructure for executing relational queries on GPUs.  ...  Red Fox is comprised of i) a language front-end for LogiQL which is a commercial query language, ii) an RA to GPU compiler, iii) optimized GPU implementation of RA operators, and iv) a supporting runtime  ...  for Cloud Computing (ISTC-CC).  ... 
doi:10.1145/2544137.2544166 fatcat:p6mzg2ugcrgi5odbrg6vz7wtwa

Red Fox

Haicheng Wu, Gregory Diamos, Tim Sheard, Molham Aref, Sean Baxter, Michael Garland, Sudhakar Yalamanchili
2014 Proceedings of Annual IEEE/ACM International Symposium on Code Generation and Optimization - CGO '14  
This paper introduces the design, implementation, and evaluation of Red Fox, a compiler and runtime infrastructure for executing relational queries on GPUs.  ...  Red Fox is comprised of i) a language front-end for LogiQL which is a commercial query language, ii) an RA to GPU compiler, iii) optimized GPU implementation of RA operators, and iv) a supporting runtime  ...  relational query processing.  ... 
doi:10.1145/2581122.2544166 fatcat:piwvqur6ubdgbobceu4uoafnnq

SORNet: Spatial Object-Centric Representations for Sequential Manipulation [article]

Wentao Yuan, Chris Paxton, Karthik Desingh, Dieter Fox
2021 arXiv   pre-print
Sequential manipulation tasks require a robot to perceive the state of an environment and plan a sequence of actions leading to a desired goal state, where the ability to reason about spatial relationships  ...  Further, we present real-world robotic experiments demonstrating the usage of the learned object embeddings in task planning for sequential manipulation.  ...  F.5 Training Hyperparameters All models are trained using binary cross-entropy loss with SGD optimizer (momentum set to 0.9) on 4 GPUs with 32G memory. The batch size for a single GPU is 512.  ... 
arXiv:2109.03891v2 fatcat:mp2cmvyg5nb3lfme3bopv2b4tm

Concurrent query processing in a GPU-based database system

Hao Li, Yi-Cheng Tu, Bo Zeng, Rashid Mehmood
2019 PLoS ONE  
In our previous work, we explored the single compute-bound kernel modeling on GPUs under NVidia's CUDA framework and provided an in-depth anatomy of the NVidia's concurrent kernel execution mechanism (  ...  Such results are verified by extensive experiments running on our microbenchmark that consists of real-world GPU queries.  ...  [18] developed a compiler and runtime infrastructure called Red Fox to execute relational queries on GPUs. Paul et al.  ... 
doi:10.1371/journal.pone.0214720 pmid:30990851 pmcid:PMC6467383 fatcat:4u2hmql235c4fkxajva5mcx6m4

DryadLINQ for Scientific Analyses

Jaliya Ekanayake, Thilina Gunarathne, Geoffrey Fox, Atilla Soner Balkir, Christophe Poulain, Nelson Araujo, Roger Barga
2009 2009 Fifth IEEE International Conference on e-Science  
Recently, Microsoft has released DryadLINQ for academic use, allowing users to experience a new programming model and a runtime that is capable of performing large scale data/compute intensive analyses  ...  In this paper, we present our experience in applying DryadLINQ for a series of scientific data analysis applications, identify their mapping to the DryadLINQ programming model, and compare their performances  ...  We would also like to thank Joe Rinkovsky from IU UITS for his dedicated support in setting up the compute clusters.  ... 
doi:10.1109/e-science.2009.53 dblp:conf/eScience/EkanayakeGFBPAB09 fatcat:f34xikkskbhvvfevazb7m7663i

Relational Learning with GPUs: Accelerating Rule Coverage

Carlos Alberto Martínez-Angeles, Haicheng Wu, Inês Dutra, Vítor Santos Costa, Jorge Buenabad-Chávez
2015 International journal of parallel programming  
Relational learning algorithms mine complex databases for interesting patterns.  ...  In this work we present the design of a relational learning system, that takes advantage of graphics processing units (GPUs) to perform the most time consuming function of the learner, rule coverage.  ...  We would also like to thank Martínez-Angeles' M.Sc. and qualification committee members for their helpful comments.  ... 
doi:10.1007/s10766-015-0364-7 fatcat:zelqchqqavaqlibv2m55vwmnqi

STORM: An Integrated Framework for Fast Joint-Space Model-Predictive Control for Reactive Manipulation [article]

Mohak Bhardwaj, Balakumar Sundaralingam, Arsalan Mousavian, Nathan Ratliff, Dieter Fox, Fabio Ramos, Byron Boots
2021 arXiv   pre-print
In this paper, we develop a system for fast, joint space sampling-based MPC for manipulators that is efficiently parallelized using GPUs.  ...  We validate our approach by deploying it on a Franka Panda robot for a variety of dynamic manipulation tasks.  ...  In Fig. 14a we present a timing benchmark for an increasing batch size of query configurations.  ... 
arXiv:2104.13542v2 fatcat:3tfrs3eh3nex7fwicravdvr62a

Big Data, Simulations and HPC Convergence [chapter]

Geoffrey Fox, Judy Qiu, Shantenu Jha, Saliya Ekanayake, Supun Kamburugamuve
2016 Lecture Notes in Computer Science  
In this paper, we study an approach to convergence for software and applications/algorithms and show what hardware architectures it suggests.  ...  This leads to 64 properties divided into 4 views, which are Problem Architecture (Macro pattern); Execution Features (Micro patterns); Data Source and Style; and finally the Processing (runtime) View.  ...  We thank Dennis Gannon for comments on an early draft.  ... 
doi:10.1007/978-3-319-49748-8_1 fatcat:u4rxtm3jzbacpeg7elwhzad454

High-performance analysis of filtered semantic graphs

Aydin Buluç, Armando Fox, John R. Gilbert, Shoaib A. Kamil, Adam Lugowski, Leonid Oliker, Samuel W. Williams
2012 Proceedings of the 21st international conference on Parallel architectures and compilation techniques - PACT '12  
High performance is a crucial consideration when executing a complex analytic query on a massive semantic graph. In a semantic graph, vertices and edges carry "attributes" of various types.  ...  Analytic queries on semantic graphs typically depend on the values of these attributes; thus, the computation must either view the graph through a filter that passes only those individual vertices and  ...  Black edges denote following, and red edges denote retweeting. Red edges are also labelled with counts and timestamps, not shown. Outline of the paper We first survey related work.  ... 
doi:10.1145/2370816.2370897 dblp:conf/IEEEpact/BulucFGKLOW12 fatcat:o2zz3sefpnezpe3hthxpcejunu

Robust Query Processing in Co-Processor-accelerated Databases

Sebastian Breß, Henning Funke, Jens Teubner
2016 Proceedings of the 2016 International Conference on Management of Data - SIGMOD '16  
However, building a robust and efficient query engine for heterogeneous co-processor environments is still a significant challenge.  ...  Heap contention occurs when multiple operators run in parallel on a co-processor and when their accumulated memory footprint exceeds the main memory capacity of the co-processor, slowing down query execution  ...  Acknowledgements We thank the anonymous reviewers of SIGMOD for their helpful comments.  ... 
doi:10.1145/2882903.2882936 dblp:conf/sigmod/BressFT16 fatcat:biocs2levbfcbpaerykvdma5ky

High-Productivity and High-Performance Analysis of Filtered Semantic Graphs

Aydin Buluc, Erika Duriakova, Armando Fox, John R. Gilbert, Shoaib Kamil, Adam Lugowski, Leonid Oliker, Samuel Williams
2013 2013 IEEE 27th International Symposium on Parallel and Distributed Processing  
High performance is a crucial consideration when executing a complex analytic query on a massive semantic graph. In a semantic graph, vertices and edges carry attributes of various types.  ...  Analytic queries on semantic graphs typically depend on the values of these attributes; thus, the computation must view the graph through a filter that passes only those individual vertices and edges of  ...  in parallel environments.  ... 
doi:10.1109/ipdps.2013.52 dblp:conf/ipps/BulucDFGKLOW13 fatcat:ovd2i3novzfwlinmw4bkhoiyji

Parallel processing of filtered queries in attributed semantic graphs

Adam Lugowski, Shoaib Kamil, Aydın Buluç, Samuel Williams, Erika Duriakova, Leonid Oliker, Armando Fox, John R. Gilbert
2015 Journal of Parallel and Distributed Computing  
Execution of complex analytic queries on massive semantic graphs is a challenging problem in big-data analytics that requires high-performance parallel computing.  ...  low-level parallel environment.  ...  The stanza related traffic corresponds to approximately 24 bytes (16 for payload and 8 for index) of DRAM traffic per processed edge.  ... 
doi:10.1016/j.jpdc.2014.08.010 fatcat:pp4tba3iv5gebc4di4i7ohfdcm

A portable benchmark suite for highly parallel data intensive query processing

Ifrah Saeed, Jeffrey Young, Sudhakar Yalamanchili
2015 Proceedings of the 2nd Workshop on Parallel Programming for Analytics Applications - PPAA 2015  
This work describes and analyzes highly parallel relational algebra primitives that are developed to focus on data warehousing queries through the use of a common OpenCL framework that can be executed  ...  data warehousing queries on accelerators.  ...  Jeffrey Vetter of ORNL's FTG group for the use of several discrete GPUs.  ... 
doi:10.1145/2726935.2726943 fatcat:pvqkzls2erc5za4avc6arc5hoa

Stadium Hashing: Scalable and Flexible Hashing on GPUs

Farzad Khorasani, Mehmet E. Belviranli, Rajiv Gupta, Laxmi N. Bhuyan
2015 2015 International Conference on Parallel Architecture and Compilation (PACT)  
Existing hashing solutions for GPUs not only impose restrictions (e.g., inability to concurrently execute insertion and retrieval operations, limitation on the size of key-value data pairs) that limit  ...  Hashing is one of the most fundamental operations that provides a means for a program to obtain fast access to large amounts of data.  ...  Red Fox [22] proposes a more complete framework consisting of compiler and run-time components for executing relational queries on GPUs.  ... 
doi:10.1109/pact.2015.13 dblp:conf/IEEEpact/KhorasaniBGB15 fatcat:jhjcgpogi5gpxcur6cl23zx5ui

General-purpose join algorithms for large graph triangle listing on heterogeneous systems

Daniel Zinn, Haicheng Wu, Jin Wang, Molham Aref, Sudhakar Yalamanchili
2016 Proceedings of the 9th Annual Workshop on General Purpose Processing using Graphics Processing Unit - GPGPU '16  
In particular, we consider an out-of-core context where graph data are available on secondary storage such as a solid-state disk (SSD) and may not fit in the CPU main memory or GPU device memory.  ...  By using one or two GPUs, we achieve additional speedups on the same graphs.  ...  ACKNOWLEDGMENTS We gratefully acknowledge the support of National Science Foundation under grant CCF-1337177 and the Intel Science and Technology Center on Cloud Computing.  ... 
doi:10.1145/2884045.2884054 dblp:conf/ppopp/ZinnWWAY16 fatcat:yo7ln5ce6ze6pmrmj3x7e33yhe
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