Exploring deep learning in phage discovery and characterization.

Network coding has recently been widely applied in various networks for system throughput improvement and/or resilience to network dynamics. However, the computational overhead introduced by the network coding operations is not negligible and has become the cornerstone for real deployment of network coding. In this paper, we exploit the computing power of contemporary Graphic Processing Units (GPUs) to accelerate the network coding operations. We proposed three parallel algorithms that maximize the parallelism of the encoding and decoding processes, i.e., the power of GPUs is fully utilized. This paper also shares our optimization design choices and our workarounds to the challenges encountered in working with GPUs. With our implementation of the algorithms, we are able to achieve up to 12 times of speedup over the highly optimized CPU counterpart, using the NVIDIA GPU and the Computer Unified Device Architecture (CUDA) programming model.

To create a fast and generic method with sufficient quality for extracting tubular structures such as blood vessels and airways from different modalities (CT, MR and US) and organs (brain, lungs and liver) by utilizing the computational power of graphic processing units (GPUs). A cropping algorithm is used to remove unnecessary data from the datasets on the GPU. A model-based tube detection filter combined with a new parallel centerline extraction algorithm and a parallelized region growing segmentation algorithm is used to extract the tubular structures completely on the GPU. Accuracy of the proposed GPU method and centerline algorithm is compared with the ridge traversal and skeletonization/thinning methods using synthetic vascular datasets. The implementation is tested on several datasets from three different modalities: airways from CT, blood vessels from MR, and 3D Doppler Ultrasound. The results show that the method is able to extract airways and vessels in 3-5 s on a modern GPU and is less sensitive to noise than other centerline extraction methods. Tubular structures such as blood vessels and airways can be extracted from various organs imaged by different modalities in a matter of seconds, even for large datasets.

The computing power of graphics processing units (GPU) has increased rapidly, and there has been extensive research on general-purpose computing on GPU (GPGPU) for cryptographic algorithms such as RSA, ECC, NTRU, and AES. With the rise of GPGPU, commodity computers have become complex heterogeneous GPU+CPU systems. This new architecture poses new challenges and opportunities in high-performance computing. In this paper, we present high-speed parallel implementations of the rainbow method, which is known as the most efficient time-memory tradeoff, in the heterogeneous GPU+CPU system. We give a complete analysis of the effect of multiple checkpoints on reducing the cost of false alarms, and take advantage of it for load balancing between GPU and CPU. Our implementation with multiple checkpoints requires no more time on average for resolving false alarms and it actually finishes earlier than generating all online chains unlike other implementations on GPU.KeywordsCryptanalysisCryptanalytic Time-Memory TradeoffRainbow MethodGPGPUCUDAHeterogeneous Computing

Clifford Algebra (CA) is a powerful mathematical language that allows for a simple and intuitive representation of geometric objects and their transformations. It has important applications in many research fields, such as computer graphics, robotics, and machine vision. Direct hardware support of Clifford data types and operators is needed to accelerate applications based on Clifford Algebra. This paper proposes a mixed software-hardware system that exploits the computational power of Graphics Processing Units (GPUs) to accelerate Clifford operations. A code generator, namely OpenCLifford, is presented that automatically generates Java and C libraries for the direct support of Clifford elements and operations as well as OpenCL kernels to be executed on the GPU. Experimental tests have been performed to evaluate the speedup of the OpenCL parallel code executed on the GPU against the baseline C code executed on the CPU. Average speedups of 47x and 27x have been measured for 3D and 5D Clifford Algebra, respectively. The paper also presents an execution analysis of an application for fractal generation showing a 35x speedup with respect to the baseline CPU execution.

The optimised computing power of graphics processing units (GPUs) is changing the way we do image analysis in the life sciences. Not just new deep learning approaches but also GPU-accelerated classical image processing techniques are becoming available to end-users with minimal coding skills. This ongoing revolution is an opportunity to synchronise image processing operations and workflows as many of them have to be rewritten for new GPU-based computing architectures. The clesperanto project is paving the path for image-analysts working with Fiji, Icy, ImageJ, Matlab, napari, Jython, Python and others to use the same language for formulating their scientific image analysis workflows in a cross-platform fashion and thus, connects the communities of multiple software ecosystems.

The advancement of Artificial Intelligence (AI) has been largely driven by improvements in hardware, particularly Graphical Processing Units (GPUs). Originally intended for graphics rendering, GPUs have become essential for AI due to their capacity for massive parallel computations. This paper explores the architectural evolution of GPUs, their applications in AI, and their impact on deep learning, natural language processing, and real-time AI systems. Additionally, challenges such as power consumption, scalability, and cost are examined, alongside emerging solutions including AI-specific accelerators, edge computing, and quantum hardware. By analyzing the interplay between GPUs and AI, this study aims to highlight the transformative role of GPUs in modern computing and future AI developments.

Modern graphics processing units (GPUs) have complex architectures that admit exceptional performance and energy efficiency for high-throughput applications. Although GPUs consume large amounts of power, their use for high-throughput applications facilitate state-of-the-art energy efficiency and performance. Consequently, continued development relies on understanding their power consumption. This work is a survey of GPU power modeling and profiling methods with increased detail on noteworthy efforts. As direct measurement of GPU power is necessary for model evaluation and parameter initiation, internal and external power sensors are discussed. Hardware counters, which are low-level tallies of hardware events, share strong correlation to power use and performance. Statistical correlation between power and performance counters has yielded worthwhile GPU power models, yet the complexity inherent to GPU architectures presents new hurdles for power modeling. Developments and challenges of counter-based GPU power modeling are discussed. Often building on the counter-based models, research efforts for GPU power simulation, which make power predictions from input code and hardware knowledge, provide opportunities for optimization in programming or architectural design. Noteworthy strides in power simulations for GPUs are included along with their performance or functional simulator counterparts when appropriate. Last, possible directions for future research are discussed.

In this paper, a complementary approach to implementing high performance real time adaptive robust multi model control systems is presented which leverages the power of graphics processing units to implement a cache of previously discovered models and controllers, which will improve the efficiency of the control loop if the system has a repeated behavior, or the process encounters several similar types of perturbances.

Abstract. This paper introduces a library of algorithms for representing cloud microphysics in numerical models. The library is written in C++, hence the name libcloudph++. In the current release, the library covers three warm-rain schemes: the single- and double-moment bulk schemes, and the particle-based scheme with Monte Carlo coalescence. The three schemes are intended for modelling frameworks of different dimensionalities and complexities ranging from parcel models to multi-dimensional cloud-resolving (e.g. large-eddy) simulations. A two-dimensional (2-D) prescribed-flow framework is used in the paper to illustrate the library features. The libcloudph++ and all its mandatory dependencies are free and open-source software. The Boost.units library is used for zero-overhead dimensional analysis of the code at compile time. The particle-based scheme is implemented using the Thrust library that allows one to leverage the power of graphics processing units (GPU), retaining the possibility of compiling the unchanged code for execution on single or multiple standard processors (CPUs). The paper includes a complete description of the programming interface (API) of the library and a performance analysis including comparison of GPU and CPU set-ups.

Outlier detection or anomaly detection is applied in numerous applications, such as fraud detection, network intrusion detection, manufacturing, and environmental monitoring. Due to the continuous and dynamic characteristics of streaming data, outlier detection over data streams becomes a very challenging task. When analyzing real-time data streams, it is typically impossible to store the entire set of data due to space limitations. Also due to the high data rates, it is necessary to produce the results in a limited amount of time. Parallel processing power of Graphics Processing Units (GPUs) can be used to accelerate the outlier detection process, and thus address the challenges of outlier detection over data streams. This paper proposes a GPU-accelerated outlier detection algorithm for continuous data streams using kernel density estimation approach. Experiments show that the proposed SOD_GPU algorithm is efficient for detecting outliers in high-dimensional, high-speed data streams, and produces results in a timely manner without compromising the outlier detection accuracy. The proposed method achieved up to 20X speedup compared to a respective multi-core CPU implementation, and the speedup increases with the number of data attributes and the input data rate.

Several problems in computational biology require the all-against-all pairwise comparisons of tens of thousands of individual biological sequences. Each such comparison can be performed with the well-known Needleman-Wunsch alignment algorithm. However, with the rapid growth of biological databases, performing all possible comparisons with this algorithm in serial becomes extremely time-consuming. The massive computational power of graphics processing units (GPUs) makes them an appealing choice for accelerating these computations. As such, CPU-GPU clusters can enable all-against-all comparisons on large datasets.

Today, CUDA is the de facto standard programming framework to exploit the computational power of graphics processing units (GPUs) to accelerate various kinds of applications. For efficient use of a large GPU-accelerated system, one important mechanism is checkpoint-restart that can be used not only to improve fault tolerance but also to optimize node/slot allocation by suspending a job on one node and migrating the job to another node. Although several checkpoint-restart implementations have been developed so far, they do not support CUDA applications or have some severe limitations for CUDA support. Hence, we present a checkpoint-restart library for CUDA that first deletes all CUDA resources before check pointing and then restores them right after check pointing. It is necessary to restore each memory chunk at the same memory address. To this end, we propose a novel technique that replays memory related API calls. The library supports both CUDA runtime API and CUDA driver API. Moreover, the library is transparent to applications, it is not necessary to recompile the applications for check pointing. This paper demonstrates that the proposed library can achieve checkpoint-restart of various applications at acceptable overheads, and the library also works for MPI applications such as HPL.

The tremendous computing power of Graphics Processing Units (GPUs) can be used to accelerate the evolution process in Genetic Programming (GP). The automatic generation of code using the GPU usually follows two different approaches: compiling each evolved or interpreting multiple programs. Both approaches, however, have performance drawbacks. In this work, we propose a novel approach where the GPU pseudo-assembly language, PTX (Parallel Thread Execution), is evolved. Evolving PTX programs is faster, since the compilation of a PTX program takes orders of magnitude less time than a CUDA program compilation on the CPU, and no interpreter is necessary. Another important aspect of our approach is that the evolution of PTX programs follows the Quantum Inspired Linear Genetic Programming (QILGP). Our approach, called QILGP3U (QILGP + GPGPU), enables the evolution on a single machine in a reasonable time, enhances the quality of the model with the use of PTX, and for big databases can be much faster than the CPU implementation.

Deep Learning (DL) has become a prominent machine learning technique due to the availability of efficient computational resources in the form of Graphics Processing Units (GPUs), large-scale datasets and a variety of models. The newer generation of GPUs are being designed with special emphasis on optimizing performance for DL applications. Also, the availability of easy-to-use DL frameworks—like PyTorch and TensorFlow— has enhanced productivity of domain experts to work on their custom DL applications from diverse domains. However, existing Deep Neural Network (DNN) training approaches may not fully utilize the newly emerging powerful GPUs like the NVIDIA A100—this is the primary issue that we address in this paper. Our motivating analyses show that the GPU utilization on NVIDIA A100 can be as low as 43% using traditional DNN training approaches for small-to-medium DL models and input data size. This paper proposes AccDP—a data-parallel distributed DNN training approach—to accelerate GPU-based DL applications. AccDP exploits the Message Passing Interface (MPI) communication library coupled with the NVIDIA’s Multi-Process Service (MPS) to increase the amount of work assigned to parallel GPUs resulting in higher utilization of compute resources. We evaluate our proposed design on different small-to-medium DL models and input sizes on the state-of-the-art HPC clusters. By injecting more parallelism into DNN training using our approach, the evaluation shows up to 58% improvement in training performance on a single GPU and up to 62% on 16 GPUs compared to regular DNN training. Furthermore, we conduct an in-depth characterization to determine the impact of several DNN training factors and best practices—including the batch size and the number of data loading workers— to optimally utilize GPU devices. To the best of our knowledge, this is the first work that explores the use of MPS and MPI to maximize the utilization of GPUs in distributed DNN training.

Multi-Agent Based Simulation (MABS) is used to study complex systems in many research domains. As the number of modeled agents is constantly growing, using General-Purpose Computing on Graphics Units (GPGPU) appears to be very promising as it allows to use the massively parallel architecture of the GPU (Graphics Processing Unit) to do High Performance Computing (HPC). However, this technology relies on a highly specialized architecture, implying a very specific programming approach. So, to benefit from GPU power, a MABS model need to be adapted to the GPU programming paradigm.