Modern　GPUs　have　shown　promising　results　in　accel-　erating　computation　intensive　and　numerical　workloads　with　limited　dynamic　data　sharing.　However,　many　real-world　applications　manifest　ample　amount　of　data　sharing　among　concurrently　executing　threads.　Often　data　sharing　requires　mutual　exclusion　mechanism　to　ensure　data　integrity　in　multithreaded　environment.　Although　modern　GPUs　provide　atomic　primitives　that　can　be　leveraged　to　construct　fine-grained　locks,　lock-based　synchronization　requires　significant　programming　efforts　to　achieve　func-　Tional　correctness.　The　massive　multithreading　and　SIMT　execution　paradigm　of　GPUs　further　extend　the　challenges　of　GPU　locks.　To　make　applications　with　dynamic　data　sharing　benefit　from　GPU　acceleration,　we　propose　a　novel　software　transac-　Tional　memory　system　for　GPU　architectures　(GPU-STM).　The　major　challenges　include　ensuring　good　scalability　with　respect　to　the　massive　multithreading　of　GPUs,　and　preventing　livelocks　caused　by　the　SIMT　execution　paradigm　of　GPUs.　To　this　end,　we　propose　(1)　a　hierarchical　valida-　Tion　technique　and　(2)　an　encounter-time　lock-sorting　mech-　Anism　to　deal　with　the　two　challenges,　respectively.　We　build　our　GPU-STM　prototype　based　on　the　commercially　avail-　Able　GPU　platform　and　runtime.　Our　real　system　based　evaluation　shows　that　GPU-STM　outperforms　coarse-grain　locks　on　GPUs　by　up　to　20x.　Copyright　©　2014　by　the　Association　for　Computing　Machinery,　Inc.　(ACM).