CRState: InKernel CheckpointRestart of OpenCL Program Execution on GPU Checkpointrestart is an important mechanism to achieve fault tolerance, load balancing and resources sharing in a preemptive system. As Graphics Processing Unit (GPU) becomes quite popular in high performance computing as well as OpenCL programs are portable across various CPUs and GPUs, checkpointrestart of OpenCL programs on GPUs is in demand. However, due to the intricacy of computation states inside GPUs, there is no effective checkpointrestart scheme for heterogeneous devices now. This paper proposes a feasible system, CRState, to achieve checkpointrestart in GPU kernels. With the assistant of a precompiler, the primitives are inserted into programs. In runtime, the computation state existing in the underlying hardware is concretized and reconstructed at application level and is ported to heterogeneous devices. Comprehensive experiments have been conducted to demonstrate CRStateu0027s feasibility and effectiveness. The experimental results also indicate that CRState has the potential to reschedule resources and balance workload across heterogeneous devices.

A general purpose contention manager for software transactions on the GPU Abstract   The Graphics Processing Unit (GPU) is now used extensively for general purpose GPU programming (GPGPU), allowing for greater exploitation of the multicore model across many application domains. This is particularly true in cloudedgefog computing, where multiple GPU enabled servers support many different end user services. This move away from the naturally parallel domain of graphics can incur significant performance issues. Unlike the CPU, code that is hindered from execution due to blockingwaiting on the GPU can affect thousands of threads, rendering the advantages of a GPU irrelevant and reducing a highly parralel environemnt down to a serial one in the worst case. In this paper we present a solution that minimises blockingwaiting in GPGPU computing using a contention manager that offsets memory conflicts across threads through thread reordering. We consider conflicts of memory not only to avoid corruption (standard for transactional memory) but also in the semantic layer of application logic (e.g., enforcing ordering to ensure money drawn from bank account occurs after all deposits). We demonstrate how our approach is successful across a number of industry benchmarks and compare our approach to the only other related solution. We also demonstrate that our approach is scalable in terms of thread numbers (a key requirement on the GPU). We believe this is the first work of its kind demonstrating a generalised conflict and semantic contention manager suitable for the scale of parralel execution found on a GPU.