Generally,　the　single　GPU　computing　method　is　utilized　for　the　conventional　radix　sort　algorithm　based　on　GPU　parallel　computing.　Nevertheless,　as　the　data　scale　grows,　the　single　GPU　sorting　algorithm　is　gradually　demonstrating　its　performance　bottlenecks.　In　the　paper,　an　efficient　radix　sort　algorithm　based　on　multi-GPU　parallel　computing　is　proposed,　which　implements　a　strategy　of　using　different　bucket　classifications　on　multiple　GPUs　to　improve　the　sorting　performance　and　efficiency　of　large-scale　datasets.　With　the　multi-GPU　parallel　computing,　more　buckets　may　be　used　for　data　classification　in　one　traversal,　effectively　reducing　data　sorting　times,　lowering　time　complexity,　and　improving　sorting　speed　and　throughput.　The　experiment　shows　that　the　algorithm　has　significantly　improved　the　operational　efficiency,　demonstrating　good　application　prospects.　Meanwhile,　the　algorithm　herein　also　presents　good　scalability,　which　can　adapt　to　the　constantly　growing　data　scale　in　the　future.　©　2024　IEEE.