Scalable Data Clustering with GPUs Andrew D. Pangborn Thesis Defense Rochester Institute of Technology Computer Engineering Department Friday, May 14 th 2010

Data Clustering

Data Clustering Cont.

Example

Flow Cytometry

Flow Cytometry Cont.

Flow Cytometry Data Sets Size of the data, motivation for GPUs / parallel processing

Parallel Computing

Trend toward multi-core, many-core architectures

GPU Architecture Trends

Tesla GPU Architecture

GPGPU

CUDA Software Stack

CUDA Programming Model

CUDA Kernel Grids / Blocks /Threads

CUDA Memory

CUDA Program Flow

C-means

C-means Parallel Implementation

EM with a Gaussian mixture model

EM Parallel Implementation

Performance Tuning Global Memory Coalescing – 1.0/1.1 vs 1.2/1.3 devices

Performance Tuning Partition Camping

Performance Tuning CUBLAS

Multi-GPU Strategy 3 Tier Parallel hierarchy – MPI, OpenMP, CUDA

Multi-GPU Strategy MapReduce-style data distribution and reduction

Multi-GPU Implementation Very little impact on GPU kernel implementations, just their inputs / grid dimensions Discuss host-code changes

Data Distribution Asynchronous MPI sends from host instead of each node reading input file from data store

Results - Kernels Speedup figures

Results - Kernels Speedup figures

Results – Overhead Time-breakdown for I/O, GPU memcpy, etc

Multi-GPU Results Amdahl’s Law vs. Gustafson’s Law – i.e. Strong vs. Weak Scaling – i.e. Fixed Problem Size vs. Fixed-Time – i.e. True Speedup vs. Scaled Speedup

Fixed Problem Size Analysis

Time-Constrained Analysis

Conclusions

Future Work

Questions?

References