Simulating complex systems may require to handle a huge number of entities, raising scalability issues. In this respect, GPGPU is a relevant approach. However, GPU programming is a very specific approach that limits both accessibility and re-usability of developed frameworks. We here present our approach for integrating GPU in TurtleKit, a multi-agent based simulation platform. Especially, we s...

GPU devices typically have a higher off-chip bandwidth than FPGA-based systems. Thus typically GPU should perform better for bandwidth-bounded massive parallel applications. In this paper we present our implementations of a 3D recursive Gaussian IIR on multicore CPU, many-core GPU and multi-FPGA platforms. Our baseline implementation on the CPU features the smallest arithmetic computation (2 MA...

In this paper, we propose an approach to obtaining enhanced performance of the Linpack benchmark on a GPU-accelerated PC cluster connected via relatively slow inter-node connections. For one node with a quad-core Intel Xeon W3520 processor and a NVIDIA Tesla C1060 GPU card, we implement a CPU–GPU parallel double-precision general matrix–matrix multiplication (dgemm) operation, and achieve a per...

• Multi-GPU and Unified Memory implementation of the Multi-Zone NAS Benchmarks. Analysis programmability performance effects Memory. per-GPU allocation have similar programming efforts. Unified-Memory version outperforms manual from 1.1x to 1.85x. GPU-based computing systems become a widely accepted solution for high-performance-computing (HPC) domain. GPUs shown highly competitive performance-...

Kernel density estimation (KDE) is a commonly used method for spatial point pattern analysis, but it computationally demanding when analyzing large datasets. GPU-based parallel computing has been adopted to address such computational challenges. The existing GPU-parallel KDE method, however, utilizes only one GPU computing. Additionally, assumes that the input data can be held in memory all at ...

Branch Prediction is a common function in nowadays microprocessors. Branch predictor is duplicated in each core of a multi/many-core processor and makes prediction for multiple concurrent running programs respectively. To evaluate the parallel branch prediction in a multi/many-core processor, existing schemes generally use a parallel simulator running on a CPU that does not have a real massive ...

Science Information Network (SINET) is a Japanese academic backbone network for more than 800 research institutions and universities. In this paper, we present multi-GPU-driven pipeline handling huge session data of SINET. Our consists ELK stack, multi-GPU server, Splunk. A server responsible two procedures: discrimination histogramming. Discrimination dividing into ingoing/outgoing with subnet...

Heterogeneous multiand many-core systems are increasingly prevalent in the desktop and mobile domains. On these systems it is common for programs to compete with co-running programs for resources. While multi-task scheduling for CPUs is a well-studied area, how to partitioning and map computing tasks onto the hetergeneous system in the presence of GPU contention (i.e. multiple programs compete ...

Even given today’s rich hardware platforms, computation-intensive algorithms and applications, such as large-scale simulations, are still challenging to run with acceptable response times. One way to increase the performance of these algorithms and applications is by using the computing power of Graphics Processing Units (GPU). However, effectively mapping distributed software models to GPU is ...