In this paper,we focus on graphical processing unit(GPU)and discuss how its architecture affects the choice of algorithm and implementation of fully-implicit petroleum reservoir simulation.In order to obtain satisfact...In this paper,we focus on graphical processing unit(GPU)and discuss how its architecture affects the choice of algorithm and implementation of fully-implicit petroleum reservoir simulation.In order to obtain satisfactory performance on new many-core architectures such as GPUs,the simulator developers must know a great deal on the specific hardware and spend a lot of time on fine tuning the code.Porting a large petroleum reservoir simulator to emerging hardware architectures is expensive and risky.We analyze major components of an in-house reservoir simulator and investigate how to port them to GPUs in a cost-effective way.Preliminary numerical experiments show that our GPU-based simulator is robust and effective.More importantly,these numerical results clearly identify the main bottlenecks to obtain ideal speedup on GPUs and possibly other many-core architectures.展开更多
基金support from LSEC.The authors would like to thank RIPED,PetroChina,for providing data for the numerical tests and support through PetroChina New-generation Reservoir Simulation Software(No.2011A-1010)the Program of Research on Continental Sedimentary Oil Reservoir Simulation(No.z121100004912001)founded by Beijing Municipal Science&Technology Commission and PetroChina Joint Research Funding No.12HT1050002654.
文摘In this paper,we focus on graphical processing unit(GPU)and discuss how its architecture affects the choice of algorithm and implementation of fully-implicit petroleum reservoir simulation.In order to obtain satisfactory performance on new many-core architectures such as GPUs,the simulator developers must know a great deal on the specific hardware and spend a lot of time on fine tuning the code.Porting a large petroleum reservoir simulator to emerging hardware architectures is expensive and risky.We analyze major components of an in-house reservoir simulator and investigate how to port them to GPUs in a cost-effective way.Preliminary numerical experiments show that our GPU-based simulator is robust and effective.More importantly,these numerical results clearly identify the main bottlenecks to obtain ideal speedup on GPUs and possibly other many-core architectures.
