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Posts

Aug, 11

Large Scale Language Modeling: Converging on 40GB of Text in Four Hours

Recent work has shown how to train Convolutional Neural Networks (CNNs) rapidly on large image datasets, then transfer the knowledge gained from these models to a variety of tasks. Following [Radford 2017], in this work, we demonstrate similar scalability and transfer for Recurrent Neural Networks (RNNs) for Natural Language tasks. By utilizing mixed precision arithmetic […]
Aug, 11

Parallax: Automatic Data-Parallel Training of Deep Neural Networks

The employment of high-performance servers and GPU accelerators for training deep neural network models have greatly accelerated recent advances in machine learning (ML). ML frameworks, such as TensorFlow, MXNet, and Caffe2, have emerged to assist ML researchers to train their models in a distributed fashion. However, correctly and efficiently utilizing multiple machines and GPUs is […]
Aug, 5

Scheduling Computation Graphs of Deep Learning Models on Manycore CPUs

For a deep learning model, efficient execution of its computation graph is key to achieving high performance. Previous work has focused on improving the performance for individual nodes of the computation graph, while ignoring the parallelization of the graph as a whole. However, we observe that running multiple operations simultaneously without interference is critical to […]
Aug, 5

Energy-based Tuning of Convolutional Neural Networks on Multi-GPUs

Deep Learning (DL) applications are gaining momentum in the realm of Artificial Intelligence, particularly after GPUs have demonstrated remarkable skills for accelerating their challenging computational requirements. Within this context, Convolutional Neural Network (CNN) models constitute a representative example of success on a wide set of complex applications, particularly on datasets where the target can be […]
Aug, 5

GPU schedulers: how fair is fair enough?

Blocking synchronisation idioms, e.g. mutexes and barriers, play an important role in concurrent programming. However, systems with semi-fair schedulers, e.g. graphics processing units (GPUs), are becoming increasingly common. Such schedulers provide varying degrees of fairness, guaranteeing enough to allow some, but not all, blocking idioms. While a number of applications that use blocking idioms do […]
Aug, 5

OpenCLIPER: an OpenCL-based C++ Framework for Overhead-Reduced Medical Image Processing and Reconstruction on Heterogeneous Devices

Medical image processing is often limited by the computational cost of the involved algorithms. Whereas dedicated computing devices (GPUs in particular) exist and do provide significant efficiency boosts, they have an extra cost of use in terms of housekeeping tasks (device selection and initialization, data streaming, synchronization with the CPU and others), which may hinder […]
Aug, 5

CRUM: Checkpoint-Restart Support for CUDA’s Unified Memory

Unified Virtual Memory (UVM) was recently introduced on recent NVIDIA GPUs. Through software and hardware support, UVM provides a coherent shared memory across the entire heterogeneous node, migrating data as appropriate. The older CUDA programming style is akin to older large-memory UNIX applications which used to directly load and unload memory segments. Newer CUDA programs […]
Jul, 28

Elementary functions: towards automatically generated, efficient, and vectorizable implementations

Elementary mathematical functions are pervasive in many high performance computing programs. However, although the mathematical libraries (libms), on which these programs rely, generally provide several flavors of the same function, these are fixed at implementation time. Hence this monolithic characteristic of libms is an obstacle for the performance of programs relying on them, because they […]
Jul, 28

Optimization of OpenCL applications on FPGA

Since Moore’s Law is over, specialized accelerators have becoming more and more trending over the years. FPGA is one of this accelerators and their "reconfigurable hardware" capabilities make it really promising. FPGA are programmed with HDL languages which is hard and time-consuming so many high-level alternatives (such HLS, OpenCL, SystemC, …) have emerged to provide […]
Jul, 28

Smoothed-Particle Hydrodynamics Models: Implementation Features on GPUs

Parallel implementation features of self-gravitating gas dynamics modeling on multiple GPUs are considered applying the GPU-Direct technology. The parallel algorithm for solving of the self-gravitating gas dynamics problem based on hybrid OpenMP-CUDA parallel programming model has been described in detail. The gas-dynamic forces are calculated by the modified SPH-method (Smoothed Particle Hydrodynamics) while the N-body […]
Jul, 28

gSMat: A Scalable Sparse Matrix-based Join for SPARQL Query Processing

Resource Description Framework (RDF) has been widely used to represent information on the web, while SPARQL is a standard query language to manipulate RDF data. Given a SPARQL query, there often exist many joins which are the bottlenecks of efficiency of query processing. Besides, the real RDF datasets often reveal strong data sparsity, which indicates […]
Jul, 28

Block-Size Independence for GPU Programs

Optimizing GPU programs by tuning execution parameters is essential to realizing the full performance potential of GPU hardware. However, many of these optimizations do not ensure correctness and subtle errors can enter while optimizing a GPU program. Further, lack of formal models and the presence of non-trivial transformations prevent verification of optimizations. In this work, […]

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