A Family of Scheduling Algorithms for Hybrid Parallel Platforms
International Journal of Foundations of Computer Science
More and more parallel computing platforms are built upon hybrid architectures combining multi-co... more More and more parallel computing platforms are built upon hybrid architectures combining multi-core processors (CPUs) and hardware accelerators like General Purpose Graphics Processing Units (GPGPUs). We present in this paper a new method for scheduling efficiently parallel applications with [Formula: see text] CPUs and [Formula: see text] GPGPUs, where each task of the application can be processed either on an usual core (CPU) or on a GPGPU. We consider the problem of scheduling [Formula: see text] independent tasks with the objective to minimize the time for completing the whole application (makespan). This problem is NP-hard, thus, we present two families of approximation algorithms that can achieve approximation ratios of [Formula: see text] or [Formula: see text] for any integer [Formula: see text] when only one GPGPU is considered, and [Formula: see text] or [Formula: see text] for [Formula: see text] GPGPUs, where [Formula: see text] is an arbitrary small value which correspo...
IEEE Transactions on Parallel and Distributed Systems, 2017
The number of parallel systems using accelerators is growing up. The technology is now mature eno... more The number of parallel systems using accelerators is growing up. The technology is now mature enough to allow sustained petaflop/s. However, reaching this performance scale requires efficient scheduling algorithms to manage the heterogeneous computing resources. We present a new approach for scheduling independent tasks on multiple CPUs and multiple GPUs. The tasks are assumed to be parallelizable on CPUs using the moldable model: the final number of cores allotted to a task can be decided and set by the scheduler. More precisely, we design an algorithm aiming at minimizing the makespan-the maximum completion time of all tasks-for this scheduling problem. The proposed algorithm combines a dual approximation scheme with a fast integer linear program (ILP). It determines both the partitioning of the tasks, i.e. whether a task should be mapped to CPUs or a GPU, and the number of CPUs allotted to a moldable task if mapped to the CPUs. A worst case analysis shows that the algorithm has an approximation ratio of 3 2 +. However, since the complexity of the ILP-based algorithm could be non-polynomial, we also present a proved polynomial-time algorithm with an approximation ratio of 2 +. We complement the theoretical analysis of our two novel algorithms with an experimental study. In these experiments, we compare our algorithms to a modified version of the classical HEFT algorithm, adapted to handle moldable tasks. The experimental results show that our algorithm with the 3 2 + approximation ratio produces significantly shorter schedules than the modified HEFT for most of the instances. In addition, the experiments provide evidence that this ILP-based algorithm is also practically able to solve larger problem instances in a reasonable amount of time.
For many years, scheduling problems have been concerned either with parallel processor systems or... more For many years, scheduling problems have been concerned either with parallel processor systems or with dedicated processors-job shop type systems. With a development of new computing architectures this partition is no longer so obvious. Multi-core (processor) computers equipped with GPU co-processors require new scheduling strategies. This paper is devoted to a characterization of this new type of scheduling problems. After a thorough introduction of the new model of a computing system, an extension of the classical notation of scheduling problems is proposed. A special attention is paid to preemptions, since this feature of the new architecture differs the most as compared with the classical model. In the paper, several scheduling algorithms, new ones and those refining classical approaches, are presented. Possible extensions of the model are also discussed.
More and more computers use hybrid architectures combining multi-core processors and hardware acc... more More and more computers use hybrid architectures combining multi-core processors and hardware accelerators like GPUs (Graphics Processing Units). We present in this paper a new method for scheduling efficiently parallel applications with m CPUs and k GPUs, where each task of the application can be processed either on a core (CPU) or on a GPU. The objective is to minimize the makespan. The corresponding scheduling problem is NP-hard, we propose an efficient approximation algorithm which achieves an approximation ratio of 4 3 + 1 3k. We first detail and analyze the method, based on a dual approximation scheme, that uses a dynamic programming scheme to balance evenly the load between the heterogeneous resources. Finally, we run some simulations based on realistic benchmarks and compare the solution obtained by a relaxed version of this method to the one provided by a classical greedy algorithm and to lower bounds on the value of the optimal makespan. This work is supported by a CNRS-Google contract and a french national program GDR-RO.
2014 43rd International Conference on Parallel Processing, 2014
Today, many high performance computing platforms use hybrid architectures combining multi-core pr... more Today, many high performance computing platforms use hybrid architectures combining multi-core processors and hardware accelerators like GPUs (Graphic Processing Units). This paper presents a new method for scheduling tasks for biological sequence comparison applications with CPUs and GPUs. This strategy is called SWDUAL and is based on a dual approximation scheme for determining which tasks are most suitable to be executed on the GPUs. The objective is to obtain fast execution time and minimize the idle time on each PE (Processing Element). It is implemented using a master-slave model. Results obtained when sequences were compared to five public genomic databases show that this method allows to reduce the execution time on hybrid platforms when compared to other public available implementations.
A Family of Scheduling Algorithms for Hybrid Parallel Platforms
International Journal of Foundations of Computer Science
More and more parallel computing platforms are built upon hybrid architectures combining multi-co... more More and more parallel computing platforms are built upon hybrid architectures combining multi-core processors (CPUs) and hardware accelerators like General Purpose Graphics Processing Units (GPGPUs). We present in this paper a new method for scheduling efficiently parallel applications with [Formula: see text] CPUs and [Formula: see text] GPGPUs, where each task of the application can be processed either on an usual core (CPU) or on a GPGPU. We consider the problem of scheduling [Formula: see text] independent tasks with the objective to minimize the time for completing the whole application (makespan). This problem is NP-hard, thus, we present two families of approximation algorithms that can achieve approximation ratios of [Formula: see text] or [Formula: see text] for any integer [Formula: see text] when only one GPGPU is considered, and [Formula: see text] or [Formula: see text] for [Formula: see text] GPGPUs, where [Formula: see text] is an arbitrary small value which correspo...
IEEE Transactions on Parallel and Distributed Systems, 2017
The number of parallel systems using accelerators is growing up. The technology is now mature eno... more The number of parallel systems using accelerators is growing up. The technology is now mature enough to allow sustained petaflop/s. However, reaching this performance scale requires efficient scheduling algorithms to manage the heterogeneous computing resources. We present a new approach for scheduling independent tasks on multiple CPUs and multiple GPUs. The tasks are assumed to be parallelizable on CPUs using the moldable model: the final number of cores allotted to a task can be decided and set by the scheduler. More precisely, we design an algorithm aiming at minimizing the makespan-the maximum completion time of all tasks-for this scheduling problem. The proposed algorithm combines a dual approximation scheme with a fast integer linear program (ILP). It determines both the partitioning of the tasks, i.e. whether a task should be mapped to CPUs or a GPU, and the number of CPUs allotted to a moldable task if mapped to the CPUs. A worst case analysis shows that the algorithm has an approximation ratio of 3 2 +. However, since the complexity of the ILP-based algorithm could be non-polynomial, we also present a proved polynomial-time algorithm with an approximation ratio of 2 +. We complement the theoretical analysis of our two novel algorithms with an experimental study. In these experiments, we compare our algorithms to a modified version of the classical HEFT algorithm, adapted to handle moldable tasks. The experimental results show that our algorithm with the 3 2 + approximation ratio produces significantly shorter schedules than the modified HEFT for most of the instances. In addition, the experiments provide evidence that this ILP-based algorithm is also practically able to solve larger problem instances in a reasonable amount of time.
For many years, scheduling problems have been concerned either with parallel processor systems or... more For many years, scheduling problems have been concerned either with parallel processor systems or with dedicated processors-job shop type systems. With a development of new computing architectures this partition is no longer so obvious. Multi-core (processor) computers equipped with GPU co-processors require new scheduling strategies. This paper is devoted to a characterization of this new type of scheduling problems. After a thorough introduction of the new model of a computing system, an extension of the classical notation of scheduling problems is proposed. A special attention is paid to preemptions, since this feature of the new architecture differs the most as compared with the classical model. In the paper, several scheduling algorithms, new ones and those refining classical approaches, are presented. Possible extensions of the model are also discussed.
More and more computers use hybrid architectures combining multi-core processors and hardware acc... more More and more computers use hybrid architectures combining multi-core processors and hardware accelerators like GPUs (Graphics Processing Units). We present in this paper a new method for scheduling efficiently parallel applications with m CPUs and k GPUs, where each task of the application can be processed either on a core (CPU) or on a GPU. The objective is to minimize the makespan. The corresponding scheduling problem is NP-hard, we propose an efficient approximation algorithm which achieves an approximation ratio of 4 3 + 1 3k. We first detail and analyze the method, based on a dual approximation scheme, that uses a dynamic programming scheme to balance evenly the load between the heterogeneous resources. Finally, we run some simulations based on realistic benchmarks and compare the solution obtained by a relaxed version of this method to the one provided by a classical greedy algorithm and to lower bounds on the value of the optimal makespan. This work is supported by a CNRS-Google contract and a french national program GDR-RO.
2014 43rd International Conference on Parallel Processing, 2014
Today, many high performance computing platforms use hybrid architectures combining multi-core pr... more Today, many high performance computing platforms use hybrid architectures combining multi-core processors and hardware accelerators like GPUs (Graphic Processing Units). This paper presents a new method for scheduling tasks for biological sequence comparison applications with CPUs and GPUs. This strategy is called SWDUAL and is based on a dual approximation scheme for determining which tasks are most suitable to be executed on the GPUs. The objective is to obtain fast execution time and minimize the idle time on each PE (Processing Element). It is implemented using a master-slave model. Results obtained when sequences were compared to five public genomic databases show that this method allows to reduce the execution time on hybrid platforms when compared to other public available implementations.
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