Performance Issues in Parallelizing Data-Intensive applications on a Multi-core Cluster Vignesh Ravi and Gagan Agrawal

OUTLINE Motivation FREERIDE Middleware Generalized Reduction structure Shared Memory Parallelization techniques Scalability results - Kmeans, Apriori & EM Performance Analysis results Related work & Conclusion

Motivation Availability of huge amount of data – Data-intensive applications Advent of multi-core Need for abstractions and parallel programming systems Best Shared Memory Parallelization (SMP) technique is still not clear.

Goals Scalability of data-intensive applications on multi-core Comparison of different shared memory parallelization (SMP) techniques Performance analysis of SMP techniques

Context: FREERIDE A middle-ware for parallelizing Data-intensive applications Motivated by difficulties in implementing parallel datamining applications Provides high-level APIs for easier parallel programming Based on an observation of similar generalized reduction among many datamining and scientific applications

FREERIDE – Core Reduction Object – A shared data structure where results from processed data instances are stored Types of Reduction Local Reduction – Reduction within a single node Global Reduction – Reduction among a cluster of nodes

Generalized Reduction structure

Parallelization Challenges Reduction object cannot be statically partitioned between threads/nodes – Data races should be handled at runtime Size of reduction object could be large – Replication can cause memory overhead Updates to reduction object is fine-grained – Locking schemes can cause significant overhead

Techniques in FREERIDE Full-replication(f-r) Locking based techniques – Full-locking (f-l) – Optimized Full-locking(o-f-l) – Cache-sensitive locking( cs-l)

Memory Layout of locking schemes

Applications Implemented on FREERIDE Apriori (Association mining) Kmeans (Clustering based) Expectation Maximization (E-M) (clustering based)

Goals in Experimental Study Scalability of data-intensive applications on multi-core Comparison of different shared memory parallelization (SMP) techniques and mpi Performance analysis of SMP techniques

Experimental setup  Each node in the cluster has: Intel Xeon E5345 CPU 2 Quad-core machine Each core 2.33GHz 6GB Main memory  Nodes in cluster are connected by Infiniband

Experiments Two sets of experiments: Comparison of scalability results for f-r, cs-l, o-f-l and mpi with k-means, Apriori and E-M – Single node – Cluster of nodes Performance analysis results with k-means, Apriori and E-M

Applications data setup Apriori – Dataset size 900MB – Support = 3%, Confidence = 9% K-means – Dataset size 6.4 GB – 3-Dimensional points – No. of clusters, 250 E-M – Dataset size 6.4 GB – 3-Dimensional points – No. of clusters, 60

Apriori (Single node)

Apriori (cluster)

k-means (single node)

K-means (cluster)

E-M (Single node)

E-M (cluster)

Performance Analysis of SMP techniques Given an application can we predict the factors that determines the best SMP technique? Why locking techniques suffer with Apriori, but competes well with other applications? What factors limit the overall scalability of data-intensive applications?

Performance Analysis setup Valgrind used for the Dynamic Binary Analysis Cachegrind used for the analysis of cache utilization

Performance Analysis Locking vs Merge Overhead

Performance Analysis (contd…) Relative L2 misses for reduction object

Performance Analysis (contd …) Total program read/write misses

Analysis Important Trade-off – Memory needs of application – Frequency of updating reduction object E-M is compute and memory intensive – Locking overhead is very low – Replication overhead is high Apriori has high update fraction and very less computation – Locking overhead is extremely high – Replication performs the best

Related Work Google Mapreduce Yahoo Hadoop Phoenix – Stanford university SALSA – Indiana university

Conclusion Replication and locking schemes can outperform each other Locking schemes have huge overhead when there is little computation between updates in ReductionObject MPI processes competes well upto 4 threads, but experiences communication overheads with 8 threads Performance analysis shows memory needs of an application and update fraction are significant factors for scalability

Thank you!!!! Questions???