Instruct: Given a paper title, retrieve paper titles that are cited by the given paper.
Query: Optimized Broadcast for Deep Learning Workloads on Dense-GPU InfiniBand Clusters: MPI or NCCL?
S-Caffe: Co-designing MPI Runtimes and Caffe for Scalable Deep Learning on Modern GPU Clusters
Infrastructure and API Extensions for Elastic Execution of MPI Applications
CIRCA-GPUs: Increasing Instruction Reuse Through Inexact Computing in GP-GPUs
Dynamic Task Parallelism with a GPU Work-Stealing Runtime System
Overview and comparison of OpenCL and CUDA technology for GPGPU
Optimus: an efficient dynamic resource scheduler for deep learning clusters
GPGPU: general-purpose computation on graphics hardware
Communication Optimization of Iterative Sparse Matrix-Vector Multiply on GPUs and FPGAs
Performance Tuning of Matrix Multiplication in OpenCL on Different GPUs and CPUs
Performance Modeling and Evaluation of Distributed Deep Learning Frameworks on GPUs
DeftNN: addressing bottlenecks for DNN execution on GPUs via synapse vector elimination and near-compute data fission
Approximate Communication: Techniques for Reducing Communication Bottlenecks in Large-Scale Parallel Systems
RABIT : A Reliable Allreduce and Broadcast Interface
An Integrated Compilation and Performance Analysis Environment for Data Parallel Programs
Measuring the Effects of Data Parallelism on Neural Network Training
Predicting statistics of asynchronous SGD parameters for a large-scale distributed deep learning system on GPU supercomputers
Interference-driven resource management for GPU-based heterogeneous clusters
Multi-GPU MapReduce on GPU Clusters
Managed communication and consistency for fast data-parallel iterative analytics
Precise Predictive Analysis for Discovering Communication Deadlocks in MPI Programs
Challenges in green computing for energy saving techniques
Genetic basis of the highly efficient yeast Kluyveromyces marxianus: complete genome sequence and transcriptome analyses
Wireless dual channel human body temperature measurement device
T-SNE-CUDA: GPU-Accelerated T-SNE and its Applications to Modern Data
One weird trick for parallelizing convolutional neural networks