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On Dynamic Load Balancing on Graphics Processors Daniel Cederman and Philippas Tsigas Chalmers University of Technology
Overview Motivation Methods Experimental evaluation Conclusion
The problem setting Work Offline Task Task Task Task Task Task Task Online Task Task Task Task
Static Load Balancing Processor Processor Processor Processor
Static Load Balancing Processor Processor Processor Processor Task Task Task Task
Static Load Balancing Processor Processor Processor Processor Task Task Task Task
Static Load Balancing Processor Processor Processor Processor Task Task Task Task Subtask Subtask Subtask Subtask
Static Load Balancing Processor Processor Processor Processor Task Task Task Task Subtask Subtask Subtask Subtask
Dynamic Load Balancing Processor Processor Processor Processor Task Task Task Task Subtask Subtask Subtask Subtask
Task sharing Check condition Work done? Done Task Set Acquire Task Try to get task Task Got task? No, retry Task Task Perform task Task New tasks? No, continue Add Task Task Add task
System Model CUDA Global Memory Gather and scatter Compare-And-Swap Fetch-And-Inc Multiprocessors Maximum number ofconcurrent thread blocks Global Memory Multi-processor Multi-processor Multi-processor Thread Block Thread Block Thread Block Thread Block Thread Block Thread Block Thread Block Thread Block Thread Block
Synchronization Blocking Uses mutual exclusion to only allow one process at a time to access the object.  Lockfree Multiple processes can access the object concurrently. At least one operation in a set of concurrent operations finishes in a finite number of its own steps. Waitfree Multiple processes can access the object concurrently. Every operation finishes in a finite number of its own steps.
Load Balancing Methods Blocking Task Queue Non-blocking Task Queue Task Stealing Static Task List
Blocking queue Free TB 1 Head TB 2 Tail TB n
Blocking queue Free TB 1 Head TB 2 Tail TB n
Blocking queue Free TB 1 Head TB 2 T1 Tail TB n
Blocking queue Free TB 1 Head TB 2 T1 Tail TB n
Blocking queue Free TB 1 Head TB 2 T1 Tail TB n
Non-blocking Queue TB 1 TB 1 Head TB 2 TB 2 T1 T2 T3 T4 Tail TB n Reference P. Tsigas and Y. Zhang, A simple, fast and scalable non-blocking concurrent FIFO queue for shared memory multiprocessor systems[SPAA01]
Non-blocking Queue TB 1 TB 1 Head TB 2 TB 2 T1 T2 T3 T4 Tail TB n
Non-blocking Queue TB 1 TB 1 Head TB 2 TB 2 T1 T2 T3 T4 Tail TB n
Non-blocking Queue TB 1 TB 1 Head TB 2 TB 2 T1 T2 T3 T4 Tail TB n
Non-blocking Queue TB 1 TB 1 Head TB 2 TB 2 T1 T2 T3 T4 T5 Tail TB n
Non-blocking Queue TB 1 TB 1 Head TB 2 TB 2 T1 T2 T3 T4 T5 Tail TB n
Task stealing T1 TB 1 T3 T2 TB 2 TB n Reference Arora N. S., Blumofe R. D., Plaxton C. G. , Thread Scheduling for Multiprogrammed Multiprocessors [SPAA 98]
Task stealing T1 T4 TB 1 T3 T2 TB 2 TB n
Task stealing T1 T4 T5 TB 1 T3 T2 TB 2 TB n
Task stealing T1 T4 TB 1 T3 T2 TB 2 TB n
Task stealing T1 TB 1 T3 T2 TB 2 TB n
Task stealing TB 1 T3 T2 TB 2 TB n
Task stealing TB 1 T2 TB 2 TB n
Static Task List In T1 T2 T3 T4
Static Task List In T1 TB 1 T2 TB 2 T3 TB 3 T4 TB 4
Static Task List In Out T1 TB 1 T2 TB 2 T3 TB 3 T4 TB 4
Static Task List In Out T1 T5 TB 1 T2 TB 2 T3 TB 3 T4 TB 4
Static Task List In Out T1 T5 TB 1 T2 T6 TB 2 T3 TB 3 T4 TB 4
Static Task List In Out T1 T5 TB 1 T2 T6 TB 2 T3 T7 TB 3 T4 TB 4
Octree Partitioning Bandwidth bound
Octree Partitioning Bandwidth bound
Octree Partitioning Bandwidth bound
Octree Partitioning Bandwidth bound
Four-in-a-row Computation intensive
Graphics Processors 8800GT 14 Multiprocessors 57 GB/sec bandwidth 9600GT 8 Multiprocessors 57 GB/sec bandwidth
Blocking Queue – Octree/9600GT
Blocking Queue – Octree/8800GT
Blocking Queue – Four-in-a-row
Non-blocking Queue – Octree/9600GT
Non-blocking Queue – Octree/8800GT
Non-blocking Queue - Four-in-a-row
Task stealing – Octree/9600GT
Task stealing – Octree/8800GT
Task stealing – Four-in-a-row
Static List
Octree Comparison
Previous work Korch M., Raubert T., A comparison of task pools for dynamic load balancing of irregular algorithms, Concurrency and Computation: Practice & Experience, 16, 2003 Heirich A., Arvo J., A competetive analysis of load balancing strategies for parallel ray tracing, Journal of Supercomputing, 12, 1998 Foley T., Sugerman J., KD-tree acceleration structures for a GPU raytracer, Graphics Hardware 2005
Conclusion Synchronization plays a significant role in dynamic load-balancing Lock-free data structures/synchronization scales well and looks promising also in the GPU general purpose programming Locks perform poorly  It is good that operations such as CAS and FAA have been introduced in the new GPUs Work stealing could outperform static load balancing
Thank you! http://www.cs.chalmers.se/~dcs

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Dynamic Load-balancing On Graphics Processors