Since a large number of irregular task sets have low resource requirements and high parallelism， the use of Graphics Processing Unit （GPU） to accelerate processing is the current mainstream. However， the existing irregular task scheduling strategies either use an exclusive GPU approach or use the traditional optimization methods to map tasks to GPU devices. The former leads to the idleness of GPU resources， and the latter cannot make maximum use of GPU computing resources. Based on the analysis of existing problems， an idea of multi-knapsack optimization was adopted to enable more irregular tasks to share GPU equipment in the best way. Firstly， according to the characteristics of GPU clusters， a distributed GPU job scheduling framework consisting of schedulers and executions was given. Then， with GPU memory as the cost， an Extended-grained Greedy Scheduling （EGS） algorithm based on GPU computing resources was designed. In the algorithm， as many irregular tasks as possible were scheduled on multiple available GPUs to maximize the use of GPU computing resources， and the problem of idle GPU resources was solved. Finally， the actual benchmark programs were used to randomly generate a target task set to verify the effectiveness of the proposed scheduling strategy. Experimental results show that， compared with the traditional greedy algorithm， the Minimum Completion Time （MCT） algorithm and the Min-min algorithm， when the number of tasks is equal to 1 000，the execution time of EGS algorithm is reduced to 58%， 64% and 80% of the original ones on average respectively， and the proposed algorithm can effectively improve the GPU resource utilization.
