Fast computation of database operations using graphics processors

By Naga K. Govindaraju, Brandon Lloyd, Ming C Lin, Dinesh Manocha. ACM SIGMOD Conference, 2004

359 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/24be673b67b1e11224f1271515f4ab4e2236be5b

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Citations in the corpus

Optimizing Data Warehousing Applications for GPUs Using Kernel Fusion/Fission (2012, background): “Further, other groups focus on designing algorithms to accelerate individual operators [1], [20], [21], [22], [23], [24].”
Mars: A MapReduce Framework on graphics processors (2008, methodology): “GPGPU has recently emerged in various applications, such as FFT [17], matrix operations [18, 19], embedded system design [10], bioinformatics [21], databases [12, 13, 16], and distributed computing projects including Folding@home [11] and Seti@home [29].”
Dandelion: a compiler and runtime for heterogeneous systems (2013, background): “Others have explored primitives for supporting GPU-based relational algebra [51, 50] and database operations [43], with emphasis on optimization and performance for operations over a very limited set of data types (primitive types).”
GPU Computing (2008, background): “Horn [13]) and nearest neighbor search [14], as well as high-performance database operations that build on special-purpose graphics hardware (called the depth and stencil buffers) and the fast sorting algorithms mentioned above [15], [16].”
Fast and approximate stream mining of quantiles and frequencies using graphics processors (2005, methodology): “[20] presented novel algorithms for predicates, boolean combinations and aggregates on commodity GPUs and obtained considerable speedups over CPU-based implementations.”
A performance study of general-purpose applications on graphics processors using CUDA (2008, methodology): “Some important database operations have also been implemented on the GPU by using pixel engines [7], and a variety of other applications, such as sequence alignment [24] and AES encryption [30] have been successfully implemented on GPUs.”
GPUTeraSort: high performance graphics co-processor sorting for large database management (2006, methodology): “GPUs have also been used as co-processors to speedup database queries [9, 18, 19, 40] and data streaming [20, 29, 41].”
Accelerating Compute-Intensive Applications with GPUs and FPGAs (2008, methodology): “and bioinformatics [9] for FPGAs, and linear algebra [12], database operations [7], K-Means [4], AES encryption [19] and n-body simulations [15] on GPUs.”
Relational joins on graphics processors (2008, background): “presented novel GPUbased algorithms for relational operators including selections, aggregations [15] as well as sorting [14], and for data mining operations such as computing frequencies and quantiles for data”