Searching CUDA code autotuning spaces with hardware performance counters: data from benchmarks running on various GPU architectures.

We have developed several autotuning benchmarks in CUDA that take into account performance-relevant source-code parameters and reach near peak-performance on various GPU architectures. We have used them during the development and evaluation of a search method for tuning space proposed in [1]. With our framework Kernel Tuning Toolkit, freely available at Github, we measured computation times and hardware performance counters on several GPUs for the complete tuning spaces of five benchmarks. These data, which we provide here, might benefit research of search algorithms for the tuning spaces of GPU codes or research of relation between applied code optimization, hardware performance counters, and GPU kernels' performance. Moreover, we describe the scripts we used for robust evaluation of our searcher and comparison to others in detail. In particular, the script that simulates the tuning, i.e., replaces time-demanding compiling and executing the tuned kernels with a quick reading of the computation time from our measured data, makes it possible to inspect the convergence of tuning search over a large number of experiments. These scripts, freely available with our other codes, make it easier to experiment with search algorithms and compare them in a robust and reproducible way. During our research, we generated models for predicting values of performance counters from values of tuning parameters of our benchmarks. Here, we provide the models themselves and describe the scripts we implemented for their training. These data might benefit researchers who want to reproduce or build on our research.

Efficient Nash Equilibrium Resource Allocation Based on Game Theory Mechanism in Cloud Computing by Using Auction.

One of the most complex issues in the cloud computing environment is the problem of resource allocation so that, on one hand, the cloud provider expects the most profitability and, on the other hand, users also expect to have the best resources at their disposal considering the budget constraints and time. In most previous work conducted, heuristic and evolutionary approaches have been used to solve this problem. Nevertheless, since the nature of this environment is based on economic methods, using such methods can decrease response time and reducing the complexity of the problem. In this paper, an auction-based method is proposed which determines the auction winner by applying game theory mechanism and holding a repetitive game with incomplete information in a non-cooperative environment. In this method, users calculate suitable price bid with their objective function during several round and repetitions and send it to the auctioneer; and the auctioneer chooses the winning player based the suggested utility function. In the proposed method, the end point of the game is the Nash equilibrium point where players are no longer inclined to alter their bid for that resource and the final bid also satisfies the auctioneer's utility function. To prove the response space convexity, the Lagrange method is used and the proposed model is simulated in the cloudsim and the results are compared with previous work. At the end, it is concluded that this method converges to a response in a shorter time, provides the lowest service level agreement violations and the most utility to the provider.