Research
- ๐ Understand Problems
- ๐ List of Research papers: Prof. Kishore Kothapalli, CSTAR, IIIT Hyderabad
- ๐ โYou and Your Researchโ by Richard W Hamming (1986)
- ๐ Proceedings Scholar Metrics (2014)
- ๐ Submitting the Thesis Evaluation Request by MS/PhD Students (IIIT Hyderabad)
- ๐ The Purpose and Process of PhD Comprehensive VIVA Examination (IIIT Hyderabad)
- ๐ Policy on stipend support for research students (IIIT Hyderabad)
- ๐ Top CSE conferences list (IIIT Hyderabad)
Lithography
Machine Learning
- ๐ Weather engineering architecture
- ๐ Graph Transformers to the MAX; Mahenโs Notes
- ๐ Efficient Parallel Algorithms for Sparse Matrix Operations on a GPU with Applications; Dharma Tejaโs Thesis (2023)
- ๐ฐ FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPU; Sheng et al. (2023)
- ๐ฐ word2vec, node2vec, graph2vec, X2vec: Towards a Theory of Vector Embeddings of Structured Data; Grohe (2020)
- ๐ฐ Machine Thinking, Fast and Slow; Bonnefon and Rahwan (2020)
- ๐ฐ Graph Attention Multi-Layer Perceptron; Zhang et al. (2022)
- ๐ฐ Learning skillful medium-range global weather forecasting; Lam et al. (2023)
- ๐ฐ Joint Partitioning and Sampling Algorithm for Scaling Graph Neural Network; Das et al. (2022)
- ๐ฐ A neural network algorithm for the no-three-in-line problem; Tsuchiya and Takefuji (1995)
- ๐ฐ Bias and Fairness in Large Language Models: A Survey; Gallegos et al. (2024)
- ๐ Introducing DRIFT Search: Combining global and local search methods to improve quality and efficiency; Whiting et al. (2024)
- ๐ GraphRAG: Unlocking LLM discovery on narrative private data; Larson and Truitt (2024)
- ๐ Machine Learning on Graphs
Graph Representation
- ๐ฐ BYO: A Unified Framework for Benchmarking Large-Scale Graph Containers; Wheatman et al. (2024)
- ๐ฐ Interface for Sparse Linear Algebra Operations; Abdelfattah et al. (2024)
- ๐ฐ Low-Latency Graph Streaming using Compressed Purely-Functional Trees; Dhulipala et al. (2019)
- ๐ฐ A Parallel Packed Memory Array to Store Dynamic Graphs; Wheatman and Xu (2021)
- ๐ฐ Packed Compressed Sparse Row: A Dynamic Graph Representation; Wheatman and Xu (2018)
- ๐ฐ STINGER: High Performance Data Structure for Streaming Graphs; Ediger et al. (2012)
- ๐ฐ cuSTINGER: Supporting Dynamic Graph Algorithms for GPUs; Green and Bader (2016)
- ๐ฐ GraSU: A Fast Graph Update Library for FPGA-based Dynamic Graph Processing; Wang et al. (2021)
Dynamic Graph Generation
SimRank
- ๐ On SimRank
- ๐ฐ SimRank - A Measure of Structural-Context Similarity; Jeh and Widom (2002)
- ๐ฐ SimRank*: effective and scalable pairwise similarity search based on graph topology; Yu et al. (2019)
- ๐ฐ Accuracy Estimate and Optimization Techniques for SimRank Computation; Lizorkin et al. (2010)
- ๐ฐ Fast Computation of SimRank for Static and Dynamic Information Networks; Li et al. (2010)
- ๐ฐ A Novel and Fast SimRank Algorithm (2016)
- ๐ฐ Accelerating pairwise SimRank estimation over static and dynamic graphs; Wang et al. (2019)
- ๐ฐ Efficient Top-K SimRank-based Similarity Join; Tao and Li (2014)
- ๐ฐ SLING: A Near-Optimal Index Structure for SimRank; Tian and Xiao (2016)
Community Detection
- ๐ Partitioning with community detection idea
- ๐ Delta modularity derivation (Louvain algorithm)
- ๐ฐ Delta-Screening: A Fast and Efficient Technique to Update Communities in Dynamic Graphs; Zarayeneh and Kalyanaraman (2021)
- ๐ฐ Community Detection on the GPU; Naim et al. (2017)
- ๐ฐ Parallel heuristics for scalable community detection; Lu et al. (2015)
- ๐ฐ Algorithms for Balanced Graph Colorings with Applications in Parallel Computing; Lu et al. (2016)
- ๐ฐ Fast Incremental Community Detection on Dynamic Graphs; Zakrzewska and Bader (2015)
- ๐ฐ Hierarchical parallel algorithm for modularity-based community detection using GPUs; Cheong et al. (2013)
- ๐ฐ CGC: Contrastive Graph Clustering for Community Detection and Tracking; Park et al. (2022)
- ๐ฐ HyDetect: A Hybrid CPU-GPU Algorithm for Community Detection; Bhowmick and Vadhiyar (2019)
- ๐ฐ A Dynamic Algorithm for Local Community Detection in Graphs; Zakrzewska and Bader (2015)
- ๐ฐ Scalable Static and Dynamic Community Detection Using Grappolo; Halappanavar et al. (2017)
- ๐ฐ Fast Community Detection Algorithm With GPUs and Multicore Architectures; Somal and Narang (2011)
- ๐ฐ GPU-Accelerated Graph Clustering via Parallel Label Propagation; Kozawa et al. (2017)
- ๐ฐ Large-Scale Graph Label Propagation on GPUs; Ye et al. (2023)
- ๐ฐ Application Areas of Community Detection: A Review; Karataล and ลahin (2018)
PageRank
- ๐ STIC-D based Algorithmic Optimizations for Monolithic PageRank
- ๐ Adjusting Datatype of Rank vector and CSR Representation with PageRank
- ๐ Parallelizing PageRank for a Volta GPU
- ๐ Dead End handling strategies for PageRank algorithm
- ๐ Rank adjustment strategies for Dynamic PageRank
- ๐ Effect of stepwise adjustment of Damping factor upon PageRank
- ๐ Adjusting PageRank parameters and Comparing results
- ยฎ๏ธ Method for node ranking in a linked database; Page (1998)
- ๐ฐ Deeper Inside PageRank; Langville and Meyer (2004)
- ๐ฐ Scaling PageRank to 100 Billion Pages; Stergiou (2020)
- ๐ฐ I/O-Efficient Techniques for Computing Pagerank; Chen et al. (2002)
- ๐ฐ Incremental Page Rank Computation on Evolving Graphs; Desikan et al. (2005)
- ๐ฐ An Improved PageRank Algorithm for Multilayer Networks; Cheriyan and Sajeev (2020)
- ๐ฐ A Generalization of the PageRank Algorithm; Bidoni et al. (2014)
- ๐ฐ STIC-D: Algorithmic Techniques For Efficient Parallel Pagerank Computation on Real-World Graphs; Garg and Kothapalli (2016)
- ๐ฐ HyPR: Hybrid Page Ranking on Evolving Graphs; Giri et al. (2020)
- ๐ฐ Design and Implementation of Parallel PageRank on Multicore Platforms; Zhou et al. (2017)
- ๐ฐ Distributed PageRank computation with improved round complexities; Luo et al. (2022)
- ๐ฐ Fast distributed PageRank computation; Das Sarma et al. (2013)
- ๐ฐ FrogWild! โ Fast PageRank Approximations on Graph Engines
- ๐ฐ Evaluation of Monte Carlo Method in PageRank
- ๐ฐ Monte Carlo methods in PageRank computation: When one iteration is sufficient; Avrachenkov et al. (2007)
- ๐ฐ Efficient PageRank Tracking in Evolving Networks; Ohsaka et al. (2015)
- ๐ฐ Inequality and inequity in networkโbased ranking and recommendation algorithms; Espรญn-Noboa et al. (2022)
- ๐๏ธ PageRank on an evolving graph; Yang (2013)
- ๐ HITS algorithm
Other Graph Algorithms
- ๐ Top Research Papers in Parallel Graph Algorithms (2023)
- ๐ Parallel algorithms for multi-source graph traversal and its applications; Seemaโs Comprehensive Report
- ๐ฐ Bounded Incremental Computation; Ramalingam (1993)
- ๐ฐ Parallel Batch-Dynamic Graphs: Algorithms and Lower Bounds; Dhulipala et al. (2020)
- ๐ฐ Practice of Streaming Processing of Dynamic Graphs: Concepts, Models, and Systems; Besta et al. (2021)
- ๐ฐ A New Parallel Algorithm for Connected Components in Dynamic Graphs; McColl et al. (2013)
- ๐ฐ DotHash: Estimating Set Similarity Metrics for Link Prediction and Document Deduplication; Nunes et al. (2023)
- ๐ฐ Path-based extensions of local link prediction methods for complex networks; Aziz et al. (2020)
- ๐ฐ Fast and Memory-Efficient Minimum Spanning Tree on the GPU; Rostrup et al. (2013)
- ๐ฐ Fast shared-memory algorithms for computing the minimum spanning forest of sparse graphs; Bader and Cong (2006)
- ๐ฐ SDP: Scalable Real-time Dynamic Graph Partitioner; Patwary et al. (2021)
- ๐ฐ A Parallel Algorithm Template for Updating Single-Source Shortest Paths in Large-Scale Dynamic Networks; Khanda et al. (2021)
- ๐ฐ Distributed coloring with O(sqrt. log n) bits; Kothapalli et al. (2006)
- ๐ฐ Vertex Reordering for Real-World Graphs and Applications: An Empirical Evaluation; Barik et al. (2020)
- ๐ฐ Practical Parallel Hypergraph Algorithms; Shun (2020)
- ๐ฐ Graph-Theoretic Problems and Their New Applications; Werner (2020)
- ๐ฐ Graph theory with applications; Bondy and Murty (1976)
- ๐๏ธ Tutorial: Large Scale Network Analytics with SNAP; Sosic and Leskovec (2014)
Computational Epidemiology
- ๐ SIR Model - for spread of disease
- ๐ฐ Evaluating Architectural Changes to Alter Pathogen Dynamics in a Dialysis Unit; Jang et al. (2019)
- ๐ฐ Investing in non-communicable disease risk factor control among adolescents worldwide - a modelling study; Watkins et al. (2019)
- ๐ฐ Care-seeking patterns for fatal non-communicable diseases among women of reproductive age in rural northwest Bangladesh; Sikder et al. (2012)
- ๐ฐ Massively Parallel Simulations of Spread of Infectious Diseases over Realistic Social Networks; Bhatele et al. (2017)
- ๐ฐ Parallel Programming Approaches for an Agent-based Simulation of Concurrent Pandemic and Seasonal Influenza Outbreaks; Soto-Ferrari et al. (2013)
- ๐ฐ Computational Epidemiology; Marathe et al. (2013)
- ๐ฐ Are noncommunicable diseases communicable?; Finlay (2020)
High Performance Computing (inc. Fault Tolerance)
- ๐ Misra-Gries algorithm for finding heavy hitters in a list of numbers, using vector instructions
- ๐ Communication in MPI
- ๐ Shared memory Parallelism
- ๐ Ordered vs. Unordered: a Comparison of Parallelism and Work-efficiency in Irregular Algorithms
- ๐ Important parameters for running MPI program with SRUN and MPIEXEC
- ๐ Checking NVIDIA Tesla V100 machine details
- ๐ Lvalues and Rvalues
- ๐ Using CMake on a CentOS 7 system
- ๐ Using CUDA GDB on a CentOS 7 sytem
- ๐ Using tee on a CentOS 7 system
- ๐ Using ts (task-spooler) of a CentOS 7 system
- ๐ Using RAPIDS GPU libraries on a CentOS 7 system
- ๐ Using numactl (NUMA control) on a CentOS7 system
- ๐ Using gedit text editor on a CentOS 7 system through SSH
- ๐ Using gcc 11 on a CentOS 7 system
- ๐ Using firewall-cmd on a CentOS 7 system
- ๐ Setting up environment of a CentOS 7 system
- ๐ Using cp (copy) on a CentOS 7 system
- ๐ Using Cilk Plus on a CentOS 7 system
- ๐ Installing CUDA on a CentOS 7 system
- ๐ Efficient Image Compression Scheme for Still Images; Manoranjanโs Thesis (2014)
- ๐ Hybrid Multicore Computing; Dip Sankar sirโs Comprehensive Report (2011)
- ๐ Statistical Network Analysis with igraph; Csรกrdi et al. (2016)
- ๐ฐ Cores that donโt count; Hochschild et al. (2021)
- ๐ฐ Fault-tolerant linear solvers via selective reliablity; Bridges et al. (2012)
- ๐ฐ Accelerating sparse matrix-vector multiplication in iterative methods using GPU; Matam and Kothapalli (2011)
- ๐ฐ A Study of BFLOAT16 for Deep Learning Training; Kalamkar et al. (2019)
- ๐ฐ Solution of a Problem in Concurrent Programming Control; Dijkstra (1965)
- ๐ฐ Fast and Efficient End-to-End Graph Processing with Shared Memory Accelerators; Mughrabi (2021)
- ๐ฐ Automated Network Performance Characterization for HPC Systems; Bartelheimer et al. (2024)
- ๐ฐ Scheduling Multithreaded Computations by Work Stealing; Blumofe and Leiserson (1999)
- ๐ฐ Fast parallel constraint satisfaction; Kirousis (1993)
- ๐ฐ ApproxBioWear: Approximating Additions for Efficient Biomedical Wearable Computing at the Edge; Kanani et al. (2021)
- ๐ฐ Big networks: A survey; Bedru et al. (2020)
- ๐ gnuplot Quick Reference; Woo and Broker (2004)
- ๐ NVIDIA Tesla V100 GPU Architecture Whitepaper
- ๐ Introduction to Level Zero API for Heterogeneous Programming; Fumero (2021)
- ๐ Variadic CRTP; Dewhurst (2017)
- ๐๏ธ Optimizing Parallel Reduction in CUDA; Harris
- ๐๏ธ Basic Computer Architecture and the Case for GPUs; Kothapalli et al. (2010)
- ๐ bfloat16 floating-point format
Distributed Systems
- ๐ Clock synchronization and TrueTime โ๏ธ
- ๐ Two-phase locking, Two-phase commit, CAP theorem, and Spanner
- ๐๏ธ Physical Clocks
CUDA Programming
- ๐ CUDA by Example: Why CUDA? Why Now?; Sanders and Kandrot (2010)
- ๐ CUDA by Example: Getting Started; Sanders and Kandrot (2010)
- ๐ CUDA by Example: Introduction to CUDA C; Sanders and Kandrot (2010)
- ๐ CUDA by Example: Parallel Programming in CUDA C; Sanders and Kandrot (2010)
- ๐ CUDA by Example: Thread Cooperation; Sanders and Kandrot (2010)
- ๐ CUDA by Example: Constant Memory and Events; Sanders and Kandrot (2010)
- ๐ CUDA by Example: Texture Memory; Sanders and Kandrot (2010)
- ๐ CUDA by Example: Graphics Interoperability; Sanders and Kandrot (2010)
- ๐ CUDA by Example: Atomics; Sanders and Kandrot (2010)
- ๐ CUDA by Example: Streams; Sanders and Kandrot (2010)
- ๐ CUDA by Example: Advanced Atomics; Sanders and Kandrot (2010)
- ๐ CUDA by Example: CUDA C on Multiple GPUs; Sanders and Kandrot (2010)
- ๐ CUDA by Example: The Final Countdown; Sanders and Kandrot (2010)
- ๐ CUDA by Example; Sanders and Kandrot (2010)
- ๐ nvGraph Library Userโs Guide
- ๐ CUDA First Programs: Computer Architecture CSE448, UAA Alaska; Mock
- ๐ CUDA Tutorial 01: Say Hello to CUDA; Sakdhnagool
- ๐ CUDA Tutorial 02: CUDA in Actions; Sakdhnagool
- ๐ RAPIDS cuGraph; Rees (2019)
- ๐๏ธ Introduction to CUDA C: Barney
Advanced Computer Architecture
- ๐ RISC-V offers simple, modular ISA; Kanter (2016)
- ๐ ECC memory
- ๐ Hybrid Memory Cube
- ๐ High Bandwidth Memory
- ๐ GDDR5 SDRAM
- ๐ GDDR4 SDRAM
- ๐ DDR SDRAM
- ๐ DDR4 SDRAM
- ๐ DDR3 SDRAM
Concurrent Data Structures
- ๐ฐ Data Structures in the Multicore Age; Shavit (2011)
- ๐ฐ Turing Lecture - The Computer Science of Concurrency - The Early Years; Lamport (2015)
- ๐ฐ The Concurrency Challenge; Hwu et al. (2008)
- ๐ฐ Real-world Concurrency; Cantrill and Bonwick (2008)
- ๐ฐ Nonblocking k-compare-single-swap; Luchangco et al. (2003)
- ๐ The Art of Multiprocessor Programming: Spin Locks and Contention; Herlihy and Shavit (2008)
- ๐ The Art of Multiprocessor Programming: Monitors and Blocking Synchronization; Herlihy and Shavit (2008)
- ๐ The Art of Multiprocessor Programming: Linked Lists: The Role of Locking; Herlihy and Shavit (2008)
- ๐ The Art of Multiprocessor Programming: Skiplists and Balanced Search; Herlihy and Shavit (2008)
- ๐ The Art of Multiprocessor Programming: Concurrent Hashing and Natural Parallelism; Herlihy and Shavit (2008)
- ๐ The Art of Multiprocessor Programming: Concurrent Stacks and Elimination; Herlihy and Shavit (2008)
- ๐๏ธ Software and the Concurrency Revolution; Sutter (2007)
- ๐ Vector processor
Engineering
Hobbies
- ๐ Mushroom training
- ๐ Amazon Wishlists: Aqua, Cloth, Copper, Exert, Forge, Glue, Laminate
- ๐ Amazon Wishlists: Pantry, Tape, Tool, Walk, Wood, Net, WAN
