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DTSTAMP:20230124T171527Z
LOCATION:C144-145
DTSTART;TZID=America/Chicago:20221118T115000
DTEND;TZID=America/Chicago:20221118T120000
UID:submissions.supercomputing.org_SC22_sess445_ws_ia112@linklings.com
SUMMARY:Accelerating Datalog Applications with cuDF
DESCRIPTION:Workshop\n\nAccelerating Datalog Applications with cuDF\n\nSho
 von, Dyken, Green, Gilray, Kumar\n\nDatalog, a bottom-up declarative logic
  programming language, has a wide variety of uses for deduction, modeling,
  and data analysis, across application domains. Datalog can be efficiently
  implemented using relational algebra primitives such as join, projection 
 and union. While, there exist several multi-threaded and multi-core implem
 entations of Datalog that target CPU-based systems, our work makes an inro
 ad towards developing a Datalog implementation for GPUs. We demonstrate th
 e feasibility of a high performance relational algebra backend for a small
  subset of Datalog applications that can effectively leverage the parallel
 ism of GPUs using cuDF.  cuDF is a library from the Rapids suite that uses
  the NVIDIA CUDA programming model for GPU parallelism. It provides simila
 r functionalities to Pandas, a popular data analysis engine. In this prese
 ntation, we analyze and evaluate the performance of cuDF versus Pandas for
  two graph mining problems implemented in Datalog, (1) triangles counting 
 and (2)  transitive closure computation.\n\nSession Format: Recorded\n\nTa
 g: Accelerator-based Architectures, Algorithms, Architectures, Big Data, D
 ata Analytics, Parallel Programming Languages and Models, Productivity Too
 ls\n\nRegistration Category: Workshop Reg Pass
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