Massively　parallel　systems,　such　as　Graphics　Processing　Units　(GPUs),　play　an　increasingly　crucial　role　in　today＇s　data-intensive　computing.　The　unique　challenges　associated　with　developing　system　software　for　massively　parallel　hardware　to　support　numerous　parallel　threads　efficiently　are　of　paramount　importance.　One　such　challenge　is　the　design　of　a　dynamic　memory　allocator　to　allocate　memory　at　runtime.　Traditionally,　memory　allocators　have　relied　on　maintaining　a　global　data　structure,　such　as　a　queue　of　free　pages.　However,　in　the　context　of　massively　parallel　systems,　accessing　such　global　data　structures　can　quickly　become　a　bottleneck　even　with　multiple　queues　in　place.　This　paper　presents　a　novel　approach　to　dynamic　memory　allocation　that　eliminates　the　need　for　a　centralized　data　structure.　Our　proposed　approach　revolves　around　letting　threads　employ　random　search　procedures　to　locate　free　pages.　Through　mathematical　proofs　and　extensive　experiments,　we　demonstrate　that　the　basic　random　search　design　achieves　lower　latency　than　the　best-known　existing　solution,　Ouroboros,　in　most　situations.　Furthermore,　we　develop　more　advanced　techniques　and　algorithms　to　tackle　the　challenge　of　warp　divergence　and　further　enhance　performance　when　free　memory　is　limited.　Building　upon　these　advancements,　our　mathematical　proofs　and　experimental　results　affirm　that　these　advanced　designs　can　yield　an　order　of　magnitude　improvement　over　the　basic　design　and　consistently　outperform　the　state-of-the-art　by　up　to　two　orders　of　magnitude.　To　illustrate　the　practical　implications　of　our　work,　we　integrate　our　memory　management　techniques　into　two　GPU　algorithms:　A　hash　join　and　a　group-by.　Both　case　studies　provide　compelling　evidence　of　our　approach＇s　pronounced　performance　gains.　©　2025　Copyright　held　by　the　owner/author(s).