A GPU-based Branch-and-Bound algorithm using Integer–Vector–Matrix data structure

Branch-and-Bound (B&B) algorithms are tree-based exploratory methods for solving combinatorial optimization problems exactly to optimality. These problems are often large in size and known to be NP-hard to solve. The construction and exploration of the B&B-tree are performed using four operators: branching, bounding, selection and pruning. Such algorithms are irregular which makes their parallel design and implementation on GPU challenging. Existing GPU-accelerated B&B algorithms perform only a part of the algorithm on the GPU and rely on the transfer of pools of subproblems across the PCI Express bus to the device. To the best of our knowledge, the algorithm presented in this paper is the first GPU-based B&B algorithm that performs all four operators on the device and subsequently avoids the data transfer bottleneck between CPU and GPU. The implementation on GPU is based on the Integer–Vector–Matrix (IVM) data structure which is used instead of a conventional linked-list to store and manage the pool of subproblems. This paper revisits the IVM-based B&B algorithm on the GPU, addressing the irregularity of the algorithm in terms of workload, memory access patterns and control flow. In particular, the focus is put on reducing thread divergence by making a judicious choice for the mapping of threads onto the data. Compared to a GPU-accelerated B&B based on a linked-list, the algorithm presented in this paper solves a set of standard flowshop instances on an average 3.3 times faster.     

Graphics processing unit‐accelerated bounding for branch‐and‐bound applied to a permutation problem using data access optimization

Branch‐and‐bound (B&B) algorithms are attractive methods for solving to optimality combinatorial optimization problems using an implicit enumeration of a dynamically built tree‐based search space. Nevertheless, they are time‐consuming when dealing with large problem instances. Therefore, pruning tree nodes (subproblems) is traditionally used as a powerful mechanism to reduce the size of the explored search space. Pruning requires to perform the bounding operation, which consists of applying a lower bound function to the subproblems generated during the exploration process. Preliminary experiments performed on the Flow‐Shop scheduling problem (FSP) have shown that the bounding operation consumes over 98% of the execution time of the B&B algorithm. In this paper, we investigate the use of graphics processing unit (GPU) computing as a major complementary way to speed up the search. We revisit the design and implementation of the parallel bounding model on GPU accelerators. The proposed approach enables data access optimization. Extensive experiments have been carried out on well‐known FSP benchmarks using an Nvidia Tesla C2050 GPU card. Compared to a CPU‐based single core execution using an Intel Core i7‐970 processor without GPU, speedups higher than 100 times faster are achieved for large problem instances. At an equivalent peak performance, GPU‐accelerated B&B is twice faster than its multi‐core counterpart. Copyright © 2013 John Wiley & Sons, Ltd.     

Generating subproblems in branch and bound algorithms for parallel machines scheduling problem

A branch and bound algorithm (B&B) has been widely used in various discrete and combinatorial optimization fields. To obtain optimal solutions as soon as possible for scheduling problems, three tools, which are branching, bounding and dominance rules, have been developed in the B&B algorithm. One of these tools, a branching is a method for generating subproblems and directly determines size of solution to be searched in the B&B algorithm. Therefore, it is very important to devise effective branching scheme for the problem.In this note, a survey of branching schemes is performed for parallel machines scheduling (PMS) problems with n independent jobs and m machines and new branching schemes that can be used for identical and unrelated PMS problems, respectively, are suggested. The suggested branching methods show that numbers of generated subproblems are much smaller than that of other methods developed earlier and therefore, it is expected that they help to reduce a lot of CPU time required to obtain optimal solutions in the B&B algorithm.     

Miss-aware LLC buffer management strategy based on heterogeneous multi-core

When multiple processor (CPU) cores and a GPU integrated together on the same chip share the last-level cache (LLC), the competition for LLC is more serious. CPU and GPU have different memory access characteristics, so that they have differences in the sensitivity of LLC capacity. For many CPU applications, a reduced share of the LLC could lead to significant performance degradation. On the contrary, GPU applications have high number of concurrent threads and they can tolerate access latency. Taking into account the GPU program memory latency tolerance characteristics, we propose an LLC buffer management strategy (buffer-for-GPU, BFG) for heterogeneous multi-core. A buffer is added on the side of LLC to filtrate streaming requests of GPU. Cache-insensitive GPU messages directly access to buffer instead of accessing to LLC, thereby filtering the GPU request and freeing up the LLC space for the CPU application. Then, for the different characteristics of CPU and GPU applications, an improved LRU replacement taking into account the recent access time and access frequency of the cache block is adopted. The cache misses-aware algorithm dynamically selects the improved LRU or LRU algorithm to fit the current operating state by comparing the miss rate of cache in buffer so that the performance of the system will be improved significantly.

     

wpa/wpa2-psk高速暴力破解器的设计和实现

针对基于单核CPU的wpa/wpa2-psk暴力破解器破解速度慢的缺点,提出一种分布式多核CPU加GPU的高速暴力破解器.采用分布式技术将密钥列表合理地分配到各台机器上,在单机上利用多核CPU和GPU形成多个计算核心并行破解,利用GPU计算密集型并行任务强大的计算能力提高破解速度.实验结果证明,该暴力破解器的破解速度相...     

A novel heterogeneous algorithm to simulate multiphase flow in porous media on multicore CPU–GPU systems

Multiphase flow implementations of the lattice Boltzmann method (LBM) are widely applied to the study of porous medium systems. In this work, we construct a new variant of the popular “color” LBM for two-phase flow in which a three-dimensional, 19-velocity (D3Q19) lattice is used to compute the momentum transport solution while a three-dimensional, seven velocity (D3Q7) lattice is used to compute the mass transport solution. Based on this formulation, we implement a novel heterogeneous GPU-accelerated algorithm in which the mass transport solution is computed by multiple shared memory CPU cores programmed using OpenMP while a concurrent solution of the momentum transport is performed using a GPU. The heterogeneous solution is demonstrated to provide speedup of 2.6×$2.6\times$ as compared to multi-core CPU solution and 1.8×$1.8\times$ compared to GPU solution due to concurrent utilization of both CPU and GPU bandwidths. Furthermore, we verify that the proposed formulation provides an accurate physical representation of multiphase flow processes and demonstrate that the approach can be applied to perform heterogeneous simulations of two-phase flow in porous media using a typical GPU-accelerated workstation.     

Parallelization of a color-entropy preprocessed Chan–Vese model for face contour detection on multi-core CPU and GPU

Face tracking is an important computer vision technology that has been widely adopted in many areas, from cell phone applications to industry robots. In this paper, we introduce a novel way to parallelize a face contour detecting application based on the color-entropy preprocessed Chan–Vese model utilizing a total variation G-norm. This particular application is a complicated and unsupervised computational method requiring a large amount of calculations. Several core parts therein are difficult to parallelize due to heavily correlated data processing among iterations and pixels.We develop a novel approach to parallelize the data-dependent core parts and significantly improve the runtime performance of the model computation. We implement the parallelized program on OpenCL for both multi-core CPU and GPU. For 640 * 480 input images, the parallelized program on a NVidia GTX970 GPU, a NVidia GTX660 GPU, and an AMD FX8530 8-core CPU is on average 18.6, 12.0 and 4.40 times faster than its single-thread C version on the AMD FX8530 CPU, respectively. Some parallelized routines have much higher performance improvement compared to the whole program. For instance, on the NVidia GTX970 GPU, the parallelized entropy filter routine is on average 74.0 times faster than its single-thread C version on the AMD FX8530 8-core CPU. We discuss the parallelization methodologies in detail, including the scalability, thread models, as well as synchronization methods for both multi-core CPU and GPU.     

A user mode CPU–GPU scheduling framework for hybrid workloads

Cloud platforms composed of multi-core CPU and many-core Graphics Processing Unit (GPU) have become powerful platforms to host incremental CPU–GPU workloads. In this paper, we study the problem of optimizing the CPU resource management while keeping the quality of service (QoS) of games. To this end, we propose vHybrid, a lightweight user mode runtime framework, in which we integrate a scheduling algorithm for GPU and two algorithms for CPU to efficiently utilize CPU resources with the control accuracy of QoS. vHybrid can maintain the desired QoS with low CPU utilization, while being able to guarantee better QoS performance with little overhead. Our evaluations show that vHybrid saves 37.29% of CPU utilization with satisfactory QoS for hybrid workloads, and reduces three orders of magnitude for QoS fluctuations, without any impact on GPU workloads.     

Providing Source Code Level Portability Between CPU and GPU with MapCG

Graphics processing units (GPU) have taken an important role in the general purpose computing market in recent years.At present,the common approach to programming GPU units is to write GPU specific cod...     

Polymer field-theory simulations on graphics processing units

We report the first CUDA™ graphics-processing-unit (GPU) implementation of the polymer field-theoretic simulation framework for determining fully fluctuating expectation values of equilibrium properties for periodic and select aperiodic polymer systems. Our implementation is suitable both for self-consistent field theory (mean-field) solutions of the field equations, and for fully fluctuating simulations using the complex Langevin approach. Running on NVIDIA^®  Tesla T20 series GPUs, we find double-precision speedups of up to 30×$30\times$ compared to single-core serial calculations on a recent reference CPU, while single-precision calculations proceed up to 60×$60\times$ faster than those on the single CPU core. Due to intensive communications overhead, an MPI implementation running on 64 CPU cores remains two times slower than a single GPU.