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Gospel - High-performance heterogeneous architectures for graph analytics

Graphs are common in a variety of situation, from social networks to financial transactions. These graphs are gold mines of information, and being able to process them is key to the success of many businesses. Real graph, however, are growing larger and larger, and traditional software and algorithms cannot satisfy the performance needs of the most demanding users.
Gospel, a research line at NECSTLab, aims at making graph processing faster and
readily available to researchers and industry, by leveraging high-performance heterogeneous and novel computer architectures. From accelerating algorithms such as PageRank by making use of modern GPUs, to extending existing frameworks for graph analysis, Gospel offers a broad array of techniques to bring graph processing to the world of high-performance computing.

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Gospel - High-performance heterogeneous architectures for graph analytics

  1. 1. Alberto Parravicini 2019-05-{19-31}, NGCX Gospel - High-performance graph analytics
  2. 2. Alberto Parravicini 23/05/2019 High-Performance Graph Analytics Graphs are a gold mine of information ● Social Networks ● Financial transactions ● Recommender Systems Real graphs are enormous ● Facebook: 2B users, Wikipedia: 200M links We need high-performance and scalable ways to process graphs 2
  3. 3. Alberto Parravicini 23/05/2019 Introducing Gospel ● High-performance heterogeneous architectures for graph analytics ● Make graph processing faster and readily available to researchers and industry Quick examples: 1. PageRank on Wikipedia in 0.2 sec 2. Real-time Entity Linking with >80% accuracy 3
  4. 4. Alberto Parravicini 23/05/2019 The Gospel Graph Labs 4 GPU Algorithm Acceleration ● PageRank, Embeddings Framework/DSL extension ● Green-Marl by Oracle Labs Embeddings & ML apps ● Entity Linking
  5. 5. Alberto Parravicini 23/05/2019 Approximating PageRank on GPU ● The workhorse of Google’s search, now used in many applications ● Computation time is a bottleneck, even with GPUs ● No need for 100% accuracy ● The ranking is what matters! ● Leverage approximate computing: ● Low precision arithmetic, loop perforation, numerical tricks 5
  6. 6. Alberto Parravicini 23/05/2019 PageRank on graphs larger than GPU memory ● Most real-world graphs are larger than GPU memory (e.g. the web!) ● Adapt PR to work in these cases ● Graph partitioning ● Data compression ● Double buffering and pipelining ● These techniques can be adapted to many algorithms, and to a multi-GPU scenario 6
  7. 7. Alberto Parravicini 23/05/2019 Accelerating embeddings primitives on GPU ● Vertex embeddings are key to perform machine learning on graphs ● Most algorithms have the same primitives: random walks, vertex sampling, etc… ● Often done on CPU, and results sent to GPU ● Our directions: ● Do everything on GPU, using modified Bread-First Visit 7
  8. 8. Alberto Parravicini 23/05/2019 Fast Entity Linking via Graph Embeddings (1/2) ● Entity Linking (EL): connect Named Entities to unique identities (e.g. Wikipedia Page) ● Lots of applications: search engines, recommender systems, chat bots 8 Labs
  9. 9. Alberto Parravicini 23/05/2019 Fast Entity Linking via Graph Embeddings (2/2) ● The first EL algorithm to leverage graph embeddings ● SoA results (>80% accuracy) with real-time latency (30 names / sec) Labs
  10. 10. Alberto Parravicini 23/05/2019 Automatic GPU code generation from graph DSL ● Writing high-performance GPU graph algorithms is difficult ● We can extend Green-Marl, a graph DSL developed by Oracle Labs ● Graph computation as linear algebra kernels ● We leverage GraphBlast, GPU library from the authors of Gunrock ● Easy transparent acceleration of many algorithms (e.g. BFS, PR) 10 Labs
  11. 11. The Gospel Folks Alberto Parravicini Francesco Sgherzi Elisa Tardini Nicolò Scipione Ivan Montalbano Rolando Brondolin Davide Bartolini Rhicheek Patra Marco Santambrogio 2019-05-{19-31}, NGCX alberto.parravicini@polimi.it
  12. 12. ● Approximating PageRank on GPU ● PageRank on graphs larger than GPU memory ● Accelerating embeddings primitives on GPU ● Fast Entity Linking via Graph Embeddings ● Automatic GPU code generation from Graph DSL Thank you! Gospel - High-performance graph analytics Alberto Parravicini 2019-05-{19-31}, NGCX alberto.parravicini@polimi.it

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