Multi-GPU MapReduce on GPU clusters
We present GPMR, our stand-alone MapReduce library that leverages the power of GPU
clusters for large-scale computing. To better utilize the GPU, we modify MapReduce by …
clusters for large-scale computing. To better utilize the GPU, we modify MapReduce by …
Dynamic proportional share scheduling in hadoop
Abstract We present the Dynamic Priority (DP) parallel task scheduler for Hadoop. It allows
users to control their allocated capacity by adjusting their spending over time. This simple …
users to control their allocated capacity by adjusting their spending over time. This simple …
Integrated design of AES (Advanced Encryption Standard) encrypter and decrypter
CC Lu, SY Tseng - Proceedings IEEE International Conference …, 2002 - ieeexplore.ieee.org
This paper proposed a method of integrating the AES encrypter and the AES decrypter into a
full functional AES crypto-engine. This method can make it a very low-complexity …
full functional AES crypto-engine. This method can make it a very low-complexity …
FPMR: MapReduce framework on FPGA
Machine learning and data mining are gaining increasing attentions of the computing
society. FPGA provides a highly parallel, low power, and flexible hardware platform for this …
society. FPGA provides a highly parallel, low power, and flexible hardware platform for this …
MapCG: Writing parallel program portable between CPU and GPU
Graphics Processing Units (GPU) have been playing an important role in the general
purpose computing market recently. The common approach to program GPU today is to …
purpose computing market recently. The common approach to program GPU today is to …
Heterogeneous task scheduling for accelerated openmp
Heterogeneous systems with CPUs and computational accelerators such as GPUs, FPGAs
or the upcoming Intel MIC are becoming mainstream. In these systems, peak performance …
or the upcoming Intel MIC are becoming mainstream. In these systems, peak performance …
Optimizing mapreduce for gpus with effective shared memory usage
Accelerators and heterogeneous architectures in general, and GPUs in particular, have
recently emerged as major players in high performance computing. For many classes of …
recently emerged as major players in high performance computing. For many classes of …
Multi-GPU volume rendering using MapReduce
In this paper we present a multi-GPU parallel volume rendering implemention built using the
MapReduce programming model. We give implementation details of the library, including …
MapReduce programming model. We give implementation details of the library, including …
Using shared memory to accelerate mapreduce on graphics processing units
Modern General Purpose Graphics Processing Units (GPGPUs) provide high degrees of
parallelism in computation and memory access, making them suitable for data parallel …
parallelism in computation and memory access, making them suitable for data parallel …
HAT: history-based auto-tuning MapReduce in heterogeneous environments
In MapReduce model, a job is divided into a series of map tasks and reduce tasks. The
execution time of the job is prolonged by some slow tasks seriously, especially in …
execution time of the job is prolonged by some slow tasks seriously, especially in …