5979

Hardware Transactional Memory for GPU Architectures

Wilson W. L. Fung, Inderpreet Singh, Andrew Brownsword, Tor M. Aamodt
Department of Computer and Electrical Engineering, University of British Columbia
44th IEEE/ACM International Symposium on Microarchitecture (MICRO-44), 2011

@article{fung2011hardware,

   title={Hardware Transactional Memory for GPU Architectures},

   author={Fung, W.W.L. and Singh, I. and Brownsword, A. and Aamodt, T.M.},

   year={2011}

}

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Graphics processor units (GPUs) are designed to efficiently exploit thread level parallelism (TLP), multiplexing execution of 1000s of concurrent threads on a relatively smaller set of single-instruction, multiple-thread (SIMT) cores to hide various long latency operations. While threads within a CUDA block/OpenCL workgroup can communicate efficiently through an intra-core scratchpad memory, threads in different blocks can only communicate via global memory accesses. Programmers wishing to exploit such communication have to consider data-races that may occur when multiple threads modify the same memory location. Recent GPUs provide a form of inter-block communication through atomic operations for single 32-bit/64-bit words. Although fine-grained locks can be constructed from these atomic operations, synchronization using locks is prone to deadlock. In this paper, we propose to solve these problems by extending GPUs to support transactional memory (TM). Major challenges include supporting 1000s of concurrent transactions and committing non-conflicting transactions in parallel. We propose KILO TM, a novel hardware TM design for GPUs that scales to 1000s of concurrent transactions. Without cache coherency hardware to depend on, it uses word-level, value-based conflict detection to avoid broadcast communication and reduce on-chip storage overhead. It employs speculative validation using a novel bloom filter organization to increase transaction commit parallelism. For a set of TM-enhanced GPU applications, KILO TM captures 59% of the performance of fine-grained locking, and is on average 128x faster than executing all transactions serially, for an estimated hardware area overhead of 0.5% of a commercial GPU.
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