GPGPUs　has　gradually　become　a　mainstream　acceleration　component　in　high-performance　computing.　The　long　latency　of　memory　operations　is　the　bottleneck　of　GPU　performance.　In　the　GPU,　multiple　threads　are　divided　into　one　warp　for　scheduling　and　execution.　The　L1　data　caches　have　little　capacity,　while　multiple　warps　share　one　small　cache.　That　makes　the　cache　suffer　a　large　amount　of　cache　contention　and　pipeline　stall.　We　propose　Locality-Based　Cache　Management　(LCM),　combined　with　the　Locality-Based　Warp　Scheduling　(LWS),　to　reduce　cache　contention　and　improve　GPU　performance.　Each　load　instruction　can　be　divided　into　three　types　according　to　locality:　only　used　once　as　streaming　data　locality,　accessed　multiple　times　in　the　same　warp　as　intra-warp　locality,　and　accessed　in　different　warps　as　inter-warp　data　locality.　According　to　the　locality　of　the　load　instruction,　LWS　applies　cache　bypass　to　the　streaming　locality　request　to　improve　the　cache　utilization　rate,　extend　inter-warp　memory　request　coalescing　to　make　full　use　of　the　inter-warp　locality,　and　combine　with　the　LWS　to　alleviate　cache　contention.　LCM　and　LWS　can　effectively　improve　cache　performance,　thereby　improving　overall　GPU　performance.　Through　experimental　evaluation,　our　LCM　and　LWS　can　obtain　an　average　performance　improvement　of　26%　over　baseline　GPU.