IBM

85 Seguidores

3 SlideShares 85 Seguidores 80 Siguiendos

I currently work on big data and distributed systems. Specifically, to accelerate machine learning algorithms using scale-up (e.g., GPU) and scale-out (e.g., Spark) systems. As an example, I built cuMF (https://github.com/wei-tan/CuMF/), a scalable matrix factorization library on GPU. As far as I know, cuMF is the fastest and can tackle the largest MF problem ever reported. CuMF can be used in recommender systems, embedding layer in deep learning, and topic model. I also worked on NoSQL (e.g., HBase) and services computing. My work and code have been incorporated into IBM patent portfolio and products such as BigInsights and Cognos. I am also a very hands-on researcher (see my GitHub p...

machine learning gpu big data matrix factorization recommender systems cloud computing services api web collaborative filtering recommendation apache spark

Actividad
Acerca de

IBM

Presentaciones

Accelerating Machine Learning Applications on Spark Using GPUs

服务计算：现状，思考与建议

S6211 - CuMF: Large-Scale Matrix Factorization on Just One Machine with GPUs

Recomendaciones

IBM Runtimes Performance Observations with Apache Spark

10 more lessons learned from building Machine Learning systems

Accelerating Machine Learning Applications on Spark Using GPUs

Justin Basilico, Research/ Engineering Manager at Netflix at MLconf SF - 11/13/15