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thesis.bib
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%Entries
%this is the original oopsla graphit paper.
@article{zhang2018graphit,
title={GraphIt: A High-Performance DSL for Graph Analytics},
author={Zhang, Yunming and Yang, Mengjiao and Baghdadi, Riyadh and Kamil, Shoaib and Shun, Julian and Amarasinghe, Saman},
journal={arXiv preprint arXiv:1805.00923},
year={2018}
}
@inproceedings{brahmakshatriya2021compiling,
title={Compiling Graph Applications for GPU s with GraphIt},
author={Brahmakshatriya, Ajay and Zhang, Yunming and Hong, Changwan and Kamil, Shoaib and Shun, Julian and Amarasinghe, Saman},
booktitle={2021 IEEE/ACM International Symposium on Code Generation and Optimization (CGO)},
pages={248--261},
year={2021},
organization={IEEE}
}
@article{zhang2019optimizing,
title={Optimizing ordered graph algorithms with GraphIt},
author={Zhang, Yunming and Brahmakshatriya, Ajay and Chen, Xinyi and Dhulipala, Laxman and Kamil, Shoaib and Amarasinghe, Saman and Shun, Julian},
journal={arXiv preprint arXiv:1911.07260},
year={2019}
}
% Original GraphLab paper
@inproceedings{low2010graphlab,
author = {Low, Yucheng and Gonzalez, Joseph and Kyrola, Aapo and Bickson, Danny and Guestrin, Carlos and Hellerstein, Joseph},
title = {GraphLab: A New Framework for Parallel Machine Learning},
year = {2010},
isbn = {9780974903965},
publisher = {AUAI Press},
address = {Arlington, Virginia, USA},
booktitle = {Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence},
pages = {340–349},
numpages = {10},
location = {Catalina Island, CA},
series = {UAI’10}
}
% Distributed GraphLab paper
@article{low2012distributed,
author = {Low, Yucheng and Bickson, Danny and Gonzalez, Joseph and Guestrin, Carlos and Kyrola, Aapo and Hellerstein, Joseph M.},
title = {Distributed GraphLab: A Framework for Machine Learning and Data Mining in the Cloud},
year = {2012},
issue_date = {April 2012},
publisher = {VLDB Endowment},
volume = {5},
number = {8},
issn = {2150-8097},
journal = {Proc. VLDB Endow.},
month = apr,
pages = {716–727},
numpages = {12}
}
% Grappa
@inproceedings{nelson2015grappa,
author = {Nelson, Jacob and Holt, Brandon and Myers, Brandon and Briggs, Preston and Ceze, Luis and Kahan, Simon and Oskin, Mark},
title = {Latency-Tolerant Software Distributed Shared Memory},
year = {2015},
isbn = {9781931971225},
publisher = {USENIX Association},
address = {USA},
booktitle = {Proceedings of the 2015 USENIX Conference on Usenix Annual Technical Conference},
pages = {291–305},
numpages = {15},
location = {Santa Clara, CA},
series = {USENIX ATC ’15}
}
% GraphChi
@inproceedings{aapo2012graphchi,
author = {Kyrola, Aapo and Blelloch, Guy and Guestrin, Carlos},
title = {GraphChi: Large-Scale Graph Computation on Just a PC},
year = {2012},
isbn = {9781931971966},
publisher = {USENIX Association},
address = {USA},
booktitle = {Proceedings of the 10th USENIX Conference on Operating Systems Design and Implementation},
pages = {31–46},
numpages = {16},
location = {Hollywood, CA, USA},
series = {OSDI’12}
}
% Pregel
@inproceedings{malewicz2010pregel,
title={Pregel: a system for large-scale graph processing},
author={Malewicz, Grzegorz and Austern, Matthew H and Bik, Aart JC and Dehnert, James C and Horn, Ilan and Leiser, Naty and Czajkowski, Grzegorz},
booktitle={Proceedings of the 2010 ACM SIGMOD International Conference on Management of data},
pages={135--146},
year={2010}
}
% Common/Runtime Entries
%this paper looks at adding a small serial hardware addition to a gpu to compact the data for GPU processing. compaction essentially constructs a bitmask and then gets the appropriate subset of the edgelist and constructs a new subset edgelist for processing. they also try to coalesce and organize the data to improve cache efficiency and deduplicate data to reduce work
@inproceedings{segura2019scu,
title={SCU: a GPU stream compaction unit for graph processing},
author={Segura, Albert and Arnau, Jose-Maria and Gonz{\'a}lez, Antonio},
booktitle={Proceedings of the 46th International Symposium on Computer Architecture},
pages={424--435},
year={2019},
organization={ACM}
}
%Here they updated their SSD to be aware of CSR graph layout. Edges for a node were stored in one SSD page whenever possible. and created a graph based lookup system.
@inproceedings{matam2019graphssd,
title={GraphSSD: graph semantics aware SSD},
author={Matam, Kiran Kumar and Koo, Gunjae and Zha, Haipeng and Tseng, Hung-Wei and Annavaram, Murali},
booktitle={Proceedings of the 46th International Symposium on Computer Architecture},
pages={116--128},
year={2019},
organization={ACM}
}
%this is one of beamer's papers on how best to measure graph system improvements. gives tradeoffs between algorithm improvements and system improvements. notes that often memory issues are latency bound since the bandwidth is rarely filled. says to report TEPS but also to report memory requests/edge and latency of memory requests.
@inproceedings{beamer2015gail,
title={GAIL: The graph algorithm iron law},
author={Beamer, Scott and Asanovi{\'c}, Krste and Patterson, David},
booktitle={Proceedings of the 5th Workshop on Irregular Applications: Architectures and Algorithms},
pages={1--4},
year={2015}
}
%this is a beamer paper that motivated GAIL. talks about how the biggest issue in graph processing is that the memory bandwidth isn't saturated. Mention using more speculative execution to execute more memory requests, we could try this or just use our blocking to increase the memory request bandwidth. Note that the input graph changes a lot of memory access behavior. “Efforts to move computation rather than data fare best” biggest limiter is instruction window.
@inproceedings{beamer2015locality,
title={Locality exists in graph processing: Workload characterization on an ivy bridge server},
author={Beamer, Scott and Asanovic, Krste and Patterson, David},
booktitle={2015 IEEE International Symposium on Workload Characterization},
pages={56--65},
year={2015},
organization={IEEE}
}
%proposes a vertex-centric graph processing architecture, support asynchrous execution (does not use strict iterations like traditional graph processing). This paper has a lot of good stats on CPU/GPU graph processing performance. Try to maintain a large active set of vertices/edges to increase MLP.
@inproceedings{ozdal2016energy,
title={Energy efficient architecture for graph analytics accelerators},
author={Ozdal, Muhammet Mustafa and Yesil, Serif and Kim, Taemin and Ayupov, Andrey and Greth, John and Burns, Steven and Ozturk, Ozcan},
booktitle={2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture (ISCA)},
pages={166--177},
year={2016},
organization={IEEE}
}
@article{ahn2016scalable,
title={A scalable processing-in-memory accelerator for parallel graph processing},
author={Ahn, Junwhan and Hong, Sungpack and Yoo, Sungjoo and Mutlu, Onur and Choi, Kiyoung},
journal={ACM SIGARCH Computer Architecture News},
volume={43},
number={3},
pages={105--117},
year={2016},
publisher={ACM}
}
@inproceedings{yan2019alleviating,
title={Alleviating Irregularity in Graph Analytics Acceleration: a Hardware/Software Co-Design Approach},
author={Yan, Mingyu and Hu, Xing and Li, Shuangchen and Basak, Abanti and Li, Han and Ma, Xin and Akgun, Itir and Feng, Yujing and Gu, Peng and Deng, Lei and others},
booktitle={Proceedings of the 52nd Annual IEEE/ACM International Symposium on Microarchitecture},
pages={615--628},
year={2019},
organization={ACM}
}
@inproceedings{zhuo2019graphq,
title={GraphQ: Scalable PIM-Based Graph Processing},
author={Zhuo, Youwei and Wang, Chao and Zhang, Mingxing and Wang, Rui and Niu, Dimin and Wang, Yanzhi and Qian, Xuehai},
booktitle={Proceedings of the 52nd Annual IEEE/ACM International Symposium on Microarchitecture},
pages={712--725},
year={2019},
organization={ACM}
}
%hardware scheduler to schedule traversal with locality preserved
@inproceedings{mukkara2018exploiting,
title={Exploiting locality in graph analytics through hardware-accelerated traversal scheduling},
author={Mukkara, Anurag and Beckmann, Nathan and Abeydeera, Maleen and Ma, Xiaosong and Sanchez, Daniel},
booktitle={2018 51st Annual IEEE/ACM International Symposium on Microarchitecture (MICRO)},
pages={1--14},
year={2018},
organization={IEEE}
}
%Focuses on designing an architecture for vertex centric graph processing. Focus on data type specific datapath specification and memory subsystem optimizations. Note that most instructions are for traversing the graph (94%) and only 6% are specific to the graph benchmark. First optimization is adding small scratchpad DRAM to the chip
@inproceedings{ham2016graphicionado,
title={Graphicionado: A high-performance and energy-efficient accelerator for graph analytics},
author={Ham, Tae Jun and Wu, Lisa and Sundaram, Narayanan and Satish, Nadathur and Martonosi, Margaret},
booktitle={2016 49th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO)},
pages={1--13},
year={2016},
organization={IEEE}
}
@inproceedings{yu2015imp,
title={IMP: Indirect memory prefetcher},
author={Yu, Xiangyao and Hughes, Christopher J and Satish, Nadathur and Devadas, Srinivas},
booktitle={Proceedings of the 48th International Symposium on Microarchitecture},
pages={178--190},
year={2015},
organization={ACM}
}
%They find that the memory bandwidth is limited due to load-load dependencies. That is, often in graph processing a memory load is dependent on the result of another memory load (i.e. need to look up edges of a vertex, then need to look up the parent array for those edge dest. vertices.)
@inproceedings{basak2019analysis,
title={Analysis and Optimization of the Memory Hierarchy for Graph Processing Workloads},
author={Basak, Abanti and Li, Shuangchen and Hu, Xing and Oh, Sang Min and Xie, Xinfeng and Zhao, Li and Jiang, Xiaowei and Xie, Yuan},
booktitle={2019 IEEE International Symposium on High Performance Computer Architecture (HPCA)},
pages={373--386},
year={2019},
organization={IEEE}
}
%graph500 benchmark for kronecker graphs
@article{murphy2010graph500,
title={Introducing the graph 500},
author={Murphy, Richard C and Wheeler, Kyle B and Barrett, Brian W and Ang, James A},
journal={Cray Users Group (CUG)},
volume={19},
pages={45--74},
year={2010}
}
@article{leskovec2010kronecker,
title={Kronecker graphs: an approach to modeling networks.},
author={Leskovec, Jure and Chakrabarti, Deepayan and Kleinberg, Jon and Faloutsos, Christos and Ghahramani, Zoubin},
journal={Journal of Machine Learning Research},
volume={11},
number={2},
year={2010}
}
%dramsim3 paper
@inproceedings{li2019dramsim3,
title={Rethinking cycle accurate DRAM simulation},
author={Li, Shang and Verdejo, Rommel S{\'a}nchez and Radojkovi{\'c}, Petar and Jacob, Bruce},
booktitle={Proceedings of the International Symposium on Memory Systems},
pages={184--191},
year={2019}
}
%Halide paper
@article{ragan2013halide,
title={Halide: a language and compiler for optimizing parallelism, locality, and recomputation in image processing pipelines},
author={Ragan-Kelley, Jonathan and Barnes, Connelly and Adams, Andrew and Paris, Sylvain and Durand, Fr{\'e}do and Amarasinghe, Saman},
journal={Acm Sigplan Notices},
volume={48},
number={6},
pages={519--530},
year={2013},
publisher={ACM New York, NY, USA}
}
%DRAMSim3 DRAM survey paper
@inproceedings{li2018ppmodernhighspeeddram,
author = {Li, Shang and Reddy, Dhiraj and Jacob, Bruce},
title = {A Performance \& Power Comparison of Modern High-Speed DRAM Architectures},
year = {2018},
isbn = {9781450364751},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
booktitle = {Proceedings of the International Symposium on Memory Systems},
pages = {341–353},
numpages = {13},
keywords = {cycle-accurate simulation, memory systems, DRAM architectures},
location = {Alexandria, Virginia, USA}, series = {MEMSYS ’18}
}
%Analysis of graph application on GPU for background
@inproceedings{xu2014graph,
title={Graph processing on GPUs: Where are the bottlenecks?},
author={Xu, Qiumin and Jeon, Hyeran and Annavaram, Murali},
booktitle={2014 IEEE International Symposium on Workload Characterization (IISWC)},
pages={140--149},
year={2014},
organization={IEEE}
}
%GPU Graph Bottlenecks
@article{shi2018graph,
title={Graph processing on GPUs: A survey},
author={Shi, Xuanhua and Zheng, Zhigao and Zhou, Yongluan and Jin, Hai and He, Ligang and Liu, Bo and Hua, Qiang-Sheng},
journal={ACM Computing Surveys (CSUR)},
volume={50},
number={6},
pages={1--35},
year={2018},
publisher={ACM New York, NY, USA}
}
% Background: GPU Graph Applications
@inproceedings{liu2016ibfs,
title={ibfs: Concurrent breadth-first search on gpus},
author={Liu, Hang and Huang, H Howie and Hu, Yang},
booktitle={Proceedings of the 2016 International Conference on Management of Data},
pages={403--416},
year={2016}
}
% Background: GPU Graph Platforms
@article{zhong2013medusa,
title={Medusa: Simplified graph processing on GPUs},
author={Zhong, Jianlong and He, Bingsheng},
journal={IEEE Transactions on Parallel and Distributed Systems},
volume={25},
number={6},
pages={1543--1552},
year={2013},
publisher={IEEE}
}
% Background: GPU Graph Platforms
@inproceedings{fu2014mapgraph,
title={MapGraph: A high level API for fast development of high performance graph analytics on GPUs},
author={Fu, Zhisong and Personick, Michael and Thompson, Bryan},
booktitle={Proceedings of Workshop on GRAph Data management Experiences and Systems},
pages={1--6},
year={2014}
}
% Background: GPU Graph Platforms
@inproceedings{wang2016gunrock,
title={Gunrock: A high-performance graph processing library on the GPU},
author={Wang, Yangzihao and Davidson, Andrew and Pan, Yuechao and Wu, Yuduo and Riffel, Andy and Owens, John D},
booktitle={Proceedings of the 21st ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming},
pages={1--12},
year={2016}
}
% Divergence issues
@inproceedings{fung2011thread,
title={Thread block compaction for efficient SIMT control flow},
author={Fung, Wilson WL and Aamodt, Tor M},
booktitle={2011 IEEE 17th International Symposium on High Performance Computer Architecture},
pages={25--36},
year={2011},
organization={IEEE}
}
%Beamer dense pull introduction paper
@inproceedings{beamer-bfs-direction,
author = {Beamer, Scott and Asanovi\'{c}, Krste and Patterson, David},
title = {Direction-Optimizing Breadth-First Search},
year = {2012},
isbn = {9781467308045},
publisher = {IEEE Computer Society Press},
address = {Washington, DC, USA},
booktitle = {Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis},
articleno = {12},
numpages = {10},
location = {Salt Lake City, Utah},
series = {SC ’12}
}
% why parallel graph processing is challenging overview
@article{lumsdaine2007challenges,
title={Challenges in parallel graph processing},
author={Lumsdaine, Andrew and Gregor, Douglas and Hendrickson, Bruce and Berry, Jonathan},
journal={Parallel Processing Letters},
volume={17},
number={01},
pages={5--20},
year={2007},
publisher={World Scientific}
}
%cilk citation
@article{blumofe1996cilk,
title={Cilk: An efficient multithreaded runtime system},
author={Blumofe, Robert D and Joerg, Christopher F and Kuszmaul, Bradley C and Leiserson, Charles E and Randall, Keith H and Zhou, Yuli},
journal={Journal of parallel and distributed computing},
volume={37},
number={1},
pages={55--69},
year={1996},
publisher={Elsevier}
}
%openmp citation
@book{chandra2001openmp,
title={Parallel programming in OpenMP},
author={Chandra, Rohit and Dagum, Leo and Kohr, David and Menon, Ramesh and Maydan, Dror and McDonald, Jeff},
year={2001},
publisher={Morgan kaufmann}
}
%fpga graph processing
%vertex centric framework
@inproceedings{dai2016fpgp,
title={Fpgp: Graph processing framework on fpga a case study of breadth-first search},
author={Dai, Guohao and Chi, Yuze and Wang, Yu and Yang, Huazhong},
booktitle={Proceedings of the 2016 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays},
pages={105--110},
year={2016}
}
%another vertex centric framework
@inproceedings{engelhardt2016gravf,
title={Gravf: A vertex-centric distributed graph processing framework on fpgas},
author={Engelhardt, Nina and So, Hayden Kwok-Hay},
booktitle={2016 26th International Conference on Field Programmable Logic and Applications (FPL)},
pages={1--4},
year={2016},
organization={IEEE}
}
%edge-centric and large external
@inproceedings{zhou2016high,
title={High-throughput and energy-efficient graph processing on FPGA},
author={Zhou, Shijie and Chelmis, Charalampos and Prasanna, Viktor K},
booktitle={2016 IEEE 24th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM)},
pages={103--110},
year={2016},
organization={IEEE}
}
%multi-fpga system, partitions edges and vertices between fpgas
@inproceedings{dai2017foregraph,
title={Foregraph: Exploring large-scale graph processing on multi-fpga architecture},
author={Dai, Guohao and Huang, Tianhao and Chi, Yuze and Xu, Ningyi and Wang, Yu and Yang, Huazhong},
booktitle={Proceedings of the 2017 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays},
pages={217--226},
year={2017}
}
%pagerank on fpga
@inproceedings{zhou2015optimizing,
title={Optimizing memory performance for fpga implementation of pagerank},
author={Zhou, Shijie and Chelmis, Charalampos and Prasanna, Viktor K},
booktitle={2015 International Conference on ReConFigurable Computing and FPGAs (ReConFig)},
pages={1--6},
year={2015},
organization={IEEE}
}
%sssp on fpga
@inproceedings{zhou2015accelerating,
title={Accelerating large-scale single-source shortest path on FPGA},
author={Zhou, Shijie and Chelmis, Charalampos and Prasanna, Viktor},
booktitle={2015 IEEE International Parallel and Distributed Processing Symposium Workshop},
pages={129--136},
year={2015},
organization={IEEE}
}
% graph coloring on gpu/xeon phi
@inproceedings{deveci2016kokkos,
title={Parallel graph coloring for manycore architectures},
author={Deveci, Mehmet and Boman, Erik G and Devine, Karen D and Rajamanickam, Sivasankaran},
booktitle={2016 IEEE International Parallel and Distributed Processing Symposium (IPDPS)},
pages={892--901},
year={2016},
organization={IEEE}
}
%kokkos portable lib
@article{edwards2012manycore,
title={Manycore performance-portability: Kokkos multidimensional array library},
author={Edwards, H Carter and Sunderland, Daniel and Porter, Vicki and Amsler, Chris and Mish, Sam},
journal={Scientific Programming},
volume={20},
number={2},
pages={89--114},
year={2012},
publisher={IOS Press}
}
%highly optimized bfs and connected components, also use kokkos for portability
@inproceedings{slota2015high,
title={High-performance graph analytics on manycore processors},
author={Slota, George M and Rajamanickam, Sivasankaran and Madduri, Kamesh},
booktitle={2015 IEEE International Parallel and Distributed Processing Symposium},
pages={17--27},
year={2015},
organization={IEEE}
}
%system to split work between CPU and MIC
@inproceedings{chen2015efficient,
title={Efficient and simplified parallel graph processing over cpu and mic},
author={Chen, Linchuan and Huo, Xin and Ren, Bin and Jain, Surabhi and Agrawal, Gagan},
booktitle={2015 IEEE International Parallel and Distributed Processing Symposium},
pages={819--828},
year={2015},
organization={IEEE}
}
%generates openmp code to run on intel phi, only do vectorization and data reuse analysis as optimizations.
@inproceedings{li2014grapid,
title={Grapid: A compilation and runtime framework for rapid prototyping of graph applications on many-core processors},
author={Li, Da and Chakradhar, Srimat and Becchi, Michela},
booktitle={2014 20th IEEE International Conference on Parallel and Distributed Systems (ICPADS)},
pages={174--182},
year={2014},
organization={IEEE}
}
% celerity
@article{davidson2018celerity,
author={S. {Davidson} and S. {Xie} and C. {Torng} and K. {Al-Hawai} and A. {Rovinski} and T. {Ajayi} and L. {Vega} and C. {Zhao} and R. {Zhao} and S. {Dai} and A. {Amarnath} and B. {Veluri} and P. {Gao} and A. {Rao} and G. {Liu} and R. K. {Gupta} and Z. {Zhang} and R. {Dreslinski} and C. {Batten} and M. B. {Taylor}},
journal={IEEE Micro},
title={The Celerity Open-Source 511-Core RISC-V Tiered Accelerator Fabric: Fast Architectures and Design Methodologies for Fast Chips},
year={2018},
volume={38},
number={2},
pages={30-41},
}
% Description of GraphJet, the system for content recommendations at Twitter
@article{sharma2016graphjet,
author = {Sharma, Aneesh and Jiang, Jerry and Bommannavar, Praveen and Larson, Brian and Lin, Jimmy},
title = {GraphJet: Real-Time Content Recommendations at Twitter},
year = {2016},
issue_date = {September 2016},
publisher = {VLDB Endowment},
volume = {9},
number = {13},
issn = {2150-8097},
journal = {Proc. VLDB Endow.},
month = sep,
pages = {1281–1292},
numpages = {12}
}
% Description of Pixie, Pinterest's content recommender system
@inproceedings{eksombatchai2018pixie,
author = {Eksombatchai, Chantat and Jindal, Pranav and Liu, Jerry Zitao and Liu, Yuchen and Sharma, Rahul and Sugnet, Charles and Ulrich, Mark and Leskovec, Jure},
title = {Pixie: A System for Recommending 3+ Billion Items to 200+ Million Users in Real-Time},
year = {2018},
isbn = {9781450356398},
publisher = {International World Wide Web Conferences Steering Committee},
address = {Republic and Canton of Geneva, CHE},
booktitle = {Proceedings of the 2018 World Wide Web Conference},
pages = {1775–1784},
numpages = {10},
location = {Lyon, France},
}
@inproceedings{lehmberg2014structure,
author = {Lehmberg, Oliver and Meusel, Robert and Bizer, Christian},
title = {Graph Structure in the Web: Aggregated by Pay-Level Domain},
year = {2014},
isbn = {9781450326223},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
booktitle = {Proceedings of the 2014 ACM Conference on Web Science},
pages = {119–128},
numpages = {10},
keywords = {network analysis, web graph, web mining, web science, world wide web, graph analysis},
location = {Bloomington, Indiana, USA},
series = {WebSci ’14}
}
@article{menkveld2018hft,
author = {Menkveld, Albert},
year = {2018},
month = {02},
pages = {1-8},
title = {High-Frequency Trading as Viewed through an Electron Microscope},
volume = {74},
journal = {Financial Analysts Journal},
doi = {10.2469/faj.v74.n2.1}
}
@article{boginski2005finance,
Abstract = {Massive datasets arise in a broad spectrum of scientific, engineering and commercial applications. In many practically important cases, a massive dataset can be represented as a very large graph with certain attributes associated with its vertices and edges. Studying the structure of this graph is essential for understanding the structural properties of the application it represents. Well-known examples of applying this approach are the Internet graph, the Web graph, and the Call graph. It turns out that the degree distributions of all these graphs can be described by the power-law model. Here we consider another important application---a network representation of the stock market. Stock markets generate huge amounts of data, which can be used for constructing the market graph reflecting the market behavior. We conduct the statistical analysis of this graph and show that it also follows the power-law model. Moreover, we detect cliques and independent sets in this graph. These special formations have a clear practical interpretation, and their analysis allows one to apply a new data mining technique of classifying financial instruments based on stock prices data, which provides a deeper insight into the internal structure of the stock market.},
Author = {Vladimir Boginski and Sergiy Butenko and Panos M. Pardalos},
Doi = {https://doi.org/10.1016/j.csda.2004.02.004},
Issn = {0167-9473},
Journal = {Computational Statistics \& Data Analysis},
Keywords = {Market graph, Stock price fluctuations, Cross-correlation, Data analysis, Graph theory, Degree distribution, Power-law model, Clustering coefficient, Clique, Independent set, Classification, Diversified portfolio},
Number = {2},
Pages = {431 - 443},
Title = {Statistical analysis of financial networks},
Url = {http://www.sciencedirect.com/science/article/pii/S0167947304000258},
Volume = {48},
Year = {2005},
Bdsk-Url-1 = {http://www.sciencedirect.com/science/article/pii/S0167947304000258},
Bdsk-Url-2 = {https://doi.org/10.1016/j.csda.2004.02.004}
}
@article{sahu2017ubiquity,
author = {Sahu, Siddhartha and Mhedhbi, Amine and Salihoglu, Semih and Lin, Jimmy and \"{O}zsu, M. Tamer},
title = {The Ubiquity of Large Graphs and Surprising Challenges of Graph Processing},
year = {2017},
issue_date = {December 2017},
publisher = {VLDB Endowment},
volume = {11},
number = {4},
issn = {2150-8097},
journal = {Proc. VLDB Endow.},
month = dec,
pages = {420–431},
numpages = {12}
}
@article{keeling2005networks,
title={Networks and epidemic models},
author={Keeling, Matt J and Eames, Ken TD},
journal={Journal of the Royal Society Interface},
volume={2},
number={4},
pages={295--307},
year={2005},
publisher={The Royal Society London}
}
@article{Ulyantsev2016Metafast,
author = {Ulyantsev, Vladimir I. and Kazakov, Sergey V. and Dubinkina, Veronika B. and Tyakht, Alexander V. and Alexeev, Dmitry G.},
title = "{MetaFast: fast reference-free graph-based comparison of shotgun metagenomic data}",
journal = {Bioinformatics},
volume = {32},
number = {18},
pages = {2760-2767},
year = {2016},
month = {06},
abstract = "{Motivation: High-throughput metagenomic sequencing has revolutionized our view on the structure and metabolic potential of microbial communities. However, analysis of metagenomic composition is often complicated by the high complexity of the community and the lack of related reference genomic sequences. As a start point for comparative metagenomic analysis, the researchers require efficient means for assessing pairwise similarity of the metagenomes (beta-diversity). A number of approaches were used to address this task, however, most of them have inherent disadvantages that limit their scope of applicability. For instance, the reference-based methods poorly perform on metagenomes from previously unstudied niches, while composition-based methods appear to be too abstract for straightforward interpretation and do not allow to identify the differentially abundant features.Results: We developed MetaFast, an approach that allows to represent a shotgun metagenome from an arbitrary environment as a modified de Bruijn graph consisting of simplified components. For multiple metagenomes, the resulting representation is used to obtain a pairwise similarity matrix. The dimensional structure of the metagenomic components preserved in our algorithm reflects the inherent subspecies-level diversity of microbiota. The method is computationally efficient and especially promising for an analysis of metagenomes from˘ novel environmental niches.Availability and Implementation: Source code and binaries are freely available for download at https://github.com/ctlab/metafast. The code is written in Java and is platform independent (tested on Linux and Windows x86\_64).Contact:[email protected] information:Supplementary data are available at Bioinformatics online.}",
issn = {1367-4803},
}
% Google TPU
@inproceedings{jouppi2017datacenter,
title={In-datacenter performance analysis of a tensor processing unit},
author={Jouppi, Norman P and Young, Cliff and Patil, Nishant and Patterson, David and Agrawal, Gaurav and Bajwa, Raminder and Bates, Sarah and Bhatia, Suresh and Boden, Nan and Borchers, Al and others},
booktitle={Proceedings of the 44th Annual International Symposium on Computer Architecture},
pages={1--12},
year={2017}
}
@inproceedings{mclaughlin2014bcongpus,
author = {McLaughlin, Adam and Bader, David A.},
title = {Scalable and High Performance Betweenness Centrality on the GPU},
year = {2014}, isbn = {9781479955008},
publisher = {IEEE Press},
booktitle = {Proceedings of the International Conference for High Performance Computing,
Networking, Storage and Analysis},
pages = {572–583},
numpages = {12},
keywords = {GPUs, parallel algorithms, graph algorithms},
location = {New Orleans, Louisana},
series = {SC ’14}
}
@article{jedec2020hbm,
url={https://www.jedec.org/standards-documents/docs/jesd235a},
publisher={JEDEC},
author={JEDEC},
year={2020},
month={Jan}
}
%gpu overview
@article{aamodt2018general,
title={General-purpose graphics processor architectures},
author={Aamodt, Tor M and Fung, Wilson Wai Lun and Rogers, Timothy G},
journal={Synthesis Lectures on Computer Architecture},
volume={13},
number={2},
pages={1--140},
year={2018},
publisher={Morgan \& Claypool Publishers}
}
% GNNs for Protein Structure Prediction
@article{tsubaki2019compound,
title={Compound--protein interaction prediction with end-to-end learning of neural networks for graphs and sequences},
author={Tsubaki, Masashi and Tomii, Kentaro and Sese, Jun},
journal={Bioinformatics},
volume={35},
number={2},
pages={309--318},
year={2019},
publisher={Oxford University Press}
}
%challenges with graph community detection on tilera
@inproceedings{chavarria2014scaling,
title={Scaling graph community detection on the tilera many-core architecture},
author={Chavarria-Miranda, Daniel and Halappanavar, Mahantesh and Kalyanaraman, Ananth},
booktitle={2014 21st International Conference on High Performance Computing (HiPC)},
pages={1--11},
year={2014},
organization={IEEE}
}
%tilera citation
@inproceedings{ramey2011tilera,
title={Tile-gx100 manycore processor: Acceleration interfaces and architecture},
author={Ramey, Carl},
booktitle={2011 IEEE Hot Chips 23 Symposium (HCS)},
pages={1--21},
year={2011},
organization={IEEE}
}
%parallela cite
@inproceedings{agathos2015parallela,
title={Targeting the parallella},
author={Agathos, Spiros N and Papadogiannakis, Alexandros and Dimakopoulos, Vassilios V},
booktitle={European Conference on Parallel Processing},
pages={662--674},
year={2015},
organization={Springer}
}
%adapteva cite
@article{gwennap2011adapteva,
title={Adapteva: More flops, less watts},
author={Gwennap, Linley},
journal={Microprocessor Report},
volume={6},
number={13},
pages={11--02},
year={2011}
}
% RAW
@inproceedings{taylor2004raw,
author={M. B. {Taylor} and J. {Psota} and A. {Saraf} and N. {Shnidman} and V. {Strumpen} and M. {Frank} and S. {Amarasinghe} and A. {Agarwal} and W. {Lee} and J. {Miller} and D. {Wentzlaff} and I. {Bratt} and B. {Greenwald} and H. {Hoffmann} and P. {Johnson} and J. {Kim}},
booktitle={Proceedings. 31st Annual International Symposium on Computer Architecture, 2004.},
title={Evaluation of the Raw microprocessor: an exposed-wire-delay architecture for ILP and streams},
year={2004},
volume={},
number={},
pages={2-13}
}
% limited by memory bandwidth, 3D mem is an option
@inproceedings{loi2010efficient,
title={An efficient distributed memory interface for many-core platform with 3D stacked DRAM},
author={Loi, Igor and Benini, Luca},
booktitle={2010 Design, Automation \& Test in Europe Conference \& Exhibition (DATE 2010)},
pages={99--104},
year={2010},
organization={IEEE}
}
%work stealing/balancing
@inproceedings{myers2012we,
title={Do we need a crystal ball for task migration?},
author={Myers, Brandon and Holt, Brandon},
booktitle={Presented as part of the 4th $\{$USENIX$\}$ Workshop on Hot Topics in Parallelism},
year={2012}
}
% survey on directory cache coherence
@inproceedings{agarwal1988cachecoherence,
author={A. {Agarwal} and R. {Simoni} and J. {Hennessy} and M. {Horowitz}},
booktitle={[1988] The 15th Annual International Symposium on Computer Architecture. Conference Proceedings},
title={An evaluation of directory schemes for cache coherence},
year={1988},
volume={},
number={},
pages={280-289},}
%pokec citation
@inproceedings{pokec,
title={Data analysis in public social networks},
author={Takac, Lubos and Zabovsky, Michal},
booktitle={International scientific conference and international workshop present day trends of innovations},
volume={1},
number={6},
year={2012}
}
%livejournal citation
@article{lj,
title={Defining and evaluating network communities based on ground-truth},
author={Yang, Jaewon and Leskovec, Jure},
journal={Knowledge and Information Systems},
volume={42},
number={1},
pages={181--213},
year={2015},
publisher={Springer}
}
@inproceedings{aasawat2018well,
title={How well do cpu, gpu and hybrid graph processing frameworks perform?},
author={Aasawat, Tanuj Kr and Reza, Tahsin and Ripeanu, Matei},
booktitle={2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)},
pages={458--466},
year={2018},
organization={IEEE}
}
@article{yang2019graphblast,
title={GraphBLAST: A high-performance linear algebra-based graph framework on the GPU},
author={Yang, Carl and Buluc, Aydin and Owens, John D},
journal={arXiv preprint arXiv:1908.01407},
year={2019}
}
@inproceedings{peng2018graphphi,
title={Graphphi: efficient parallel graph processing on emerging throughput-oriented architectures},
author={Peng, Zhen and Powell, Alexander and Wu, Bo and Bicer, Tekin and Ren, Bin},
booktitle={Proceedings of the 27th International Conference on Parallel Architectures and Compilation Techniques},
pages={1--14},
year={2018}
}
@article{meyer2003delta,
title={$\Delta$-stepping: a parallelizable shortest path algorithm},
author={Meyer, Ulrich and Sanders, Peter},
journal={Journal of Algorithms},
volume={49},
number={1},
pages={114--152},
year={2003},
publisher={Elsevier}
}
%ms-bfs
@article{then2014more,
title={The more the merrier: Efficient multi-source graph traversal},
author={Then, Manuel and Kaufmann, Moritz and Chirigati, Fernando and Hoang-Vu, Tuan-Anh and Pham, Kien and Kemper, Alfons and Neumann, Thomas and Vo, Huy T},
journal={Proceedings of the VLDB Endowment},
volume={8},
number={4},
pages={449--460},
year={2014},
publisher={VLDB Endowment}
}
@phdthesis{beamer2016thesis,
title={Understanding and improving graph algorithm performance},
author={Beamer, Scott},
year={2016},
school={UC Berkeley}
}
@article{nodehi2018tigr,
title={Tigr: Transforming irregular graphs for gpu-friendly graph processing},
author={Nodehi Sabet, Amir Hossein and Qiu, Junqiao and Zhao, Zhijia},
journal={ACM SIGPLAN Notices},
volume={53},
number={2},
pages={622--636},
year={2018},
publisher={ACM New York, NY, USA}
}
@inproceedings{khorasani2014cusha,
title={CuSha: vertex-centric graph processing on GPUs},
author={Khorasani, Farzad and Vora, Keval and Gupta, Rajiv and Bhuyan, Laxmi N},
booktitle={Proceedings of the 23rd international symposium on High-performance parallel and distributed computing},
pages={239--252},
year={2014}
}
@inproceedings{shun2013ligra,
title={Ligra: a lightweight graph processing framework for shared memory},
author={Shun, Julian and Blelloch, Guy E},
booktitle={Proceedings of the 18th ACM SIGPLAN symposium on Principles and practice of parallel programming},
pages={135--146},
year={2013}
}
@inproceedings{nguyen2013lightweight,
title={A lightweight infrastructure for graph analytics},
author={Nguyen, Donald and Lenharth, Andrew and Pingali, Keshav},
booktitle={Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles},
pages={456--471},
year={2013}
}
@inproceedings{brahmakshatriya2021taming,
title={Taming the Zoo: The Unified GraphIt Compiler Framework for Novel Architectures},
author={Brahmakshatriya, Ajay and Furst, Emily and Ying, Victor A and Hsu, Claire and Hong, Changwan and Ruttenberg, Max and Zhang, Yunming and Jung, Dai Cheol and Richmond, Dustin and Taylor, Michael B and others},
booktitle={Appears in the Proceedings of the 48th Annual International Symposium on Computer Architecture (ISCA), 2021},
year={2021}
}
@techreport{page1999pagerank,
title={The PageRank citation ranking: Bringing order to the web.},
author={Page, Lawrence and Brin, Sergey and Motwani, Rajeev and Winograd, Terry},
year={1999},
institution={Stanford InfoLab}
}
@inproceedings{kohlschutter2006efficient,
title={Efficient parallel computation of pagerank},
author={Kohlsch{\"u}tter, Christian and Chirita, Paul-Alexandru and Nejdl, Wolfgang},
booktitle={European Conference on Information Retrieval},
pages={241--252},
year={2006},
organization={Springer}
}
@article{brandes2001faster,
title={A faster algorithm for betweenness centrality},
author={Brandes, Ulrik},
journal={Journal of mathematical sociology},
volume={25},
number={2},
pages={163--177},
year={2001},
publisher={Taylor \& Francis}
}
%other kronecker citation
@inproceedings{leskovec2005realistic,
title={Realistic, mathematically tractable graph generation and evolution, using kronecker multiplication},
author={Leskovec, Jurij and Chakrabarti, Deepayan and Kleinberg, Jon and Faloutsos, Christos},
booktitle={European conference on principles of data mining and knowledge discovery},
pages={133--145},
year={2005},
organization={Springer}
}
@inproceedings{boldi2011layered,
title={Layered label propagation: A multiresolution coordinate-free ordering for compressing social networks},
author={Boldi, Paolo and Rosa, Marco and Santini, Massimo and Vigna, Sebastiano},
booktitle={Proceedings of the 20th international conference on World Wide Web},
pages={587--596},
year={2011}
}
@inproceedings{boldi2004webgraph,
title={The webgraph framework I: compression techniques},
author={Boldi, Paolo and Vigna, Sebastiano},
booktitle={Proceedings of the 13th international conference on World Wide Web},
pages={595--602},
year={2004}
}
@article{davis2011university,
title={The University of Florida sparse matrix collection},
author={Davis, Timothy A and Hu, Yifan},
journal={ACM Transactions on Mathematical Software (TOMS)},
volume={38},
number={1},
pages={1--25},
year={2011},
publisher={ACM New York, NY, USA}
}
@misc{road-graph,
author = {Camil Demetrescu and Andrew Goldberg and David Johnson},
title = {9th {DIMACS} implementation challenge - shortest paths},
howpublished = {http://www.dis.uniroma1.it/challenge9/},
}
@misc{snapnets,
author = {Jure Leskovec and Andrej Krevl},
title = {{SNAP Datasets}: {Stanford} Large Network Dataset Collection},
howpublished = {\url{http://snap.stanford.edu/data}},
}
@inproceedings{mislove2007measurement,
title={Measurement and analysis of online social networks},
author={Mislove, Alan and Marcon, Massimiliano and Gummadi, Krishna P and Druschel, Peter and Bhattacharjee, Bobby},
booktitle={Proceedings of the 7th ACM SIGCOMM conference on Internet measurement},
pages={29--42},
year={2007}
}
@article{barabasi1999emergence,
title={Emergence of scaling in random networks},
author={Barab{\'a}si, Albert-L{\'a}szl{\'o} and Albert, R{\'e}ka},
journal={science},
volume={286},
number={5439},
pages={509--512},
year={1999},
publisher={American Association for the Advancement of Science}
}
@inproceedings{kwak2010twitter,
title={What is Twitter, a social network or a news media?},
author={Kwak, Haewoon and Lee, Changhyun and Park, Hosung and Moon, Sue},
booktitle={Proceedings of the 19th international conference on World wide web},
pages={591--600},
year={2010}
}
@article{pereira2004detection,
title={Detection of functional modules from protein interaction networks},
author={Pereira-Leal, Jose B and Enright, Anton J and Ouzounis, Christos A},
journal={PROTEINS: Structure, Function, and Bioinformatics},
volume={54},
number={1},
pages={49--57},
year={2004},
publisher={Wiley Online Library}
}
@article{watts1998collective,
title={Collective dynamics of ‘small-world’networks},