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Posts

Nov, 20

Challenges and Techniques for Transparent Acceleration of Unmodified Big Data Applications

The ever-increasing demand for high-performance Big Data analytics and data processing has paved the way for heterogeneous hardware accelerators, such as Graphics Processing Units (GPUs) and Field Programmable Gate Arrays (FPGAs), to be integrated into modern Big Data platforms. Currently, this integration comes at the cost of programmability, as the end-user Application Programming Interface (API) […]
Nov, 20

Going green: optimizing GPUs for energy efficiency through model-steered auto-tuning

Graphics Processing Units (GPUs) have revolutionized the computing landscape over the past decade. However, the growing energy demands of data centres and computing facilities equipped with GPUs come with significant capital and environmental costs. The energy consumption of GPU applications greatly depend on how well they are optimized. Auto-tuning is an effective and commonly applied […]
Nov, 20

TorchOpt: An Efficient Library for Differentiable Optimization

Recent years have witnessed the booming of various differentiable optimization algorithms. These algorithms exhibit different execution patterns, and their execution needs massive computational resources that go beyond a single CPU and GPU. Existing differentiable optimization libraries, however, cannot support efficient algorithm development and multi-CPU/GPU execution, making the development of differentiable optimization algorithms often cumbersome and […]
Nov, 13

Capturing the Memory Topology of GPUs

Optimizing program code is an essential process for High-Performance Computing and in general. Due to a trend in the last years of employing graphics cards as accelerators for systems and due to a universal gain of the importance of GPUs, optimizing GPU code is crucial in order to achieve the best possible performance of a […]
Nov, 13

A Study on Neural-based Code Summarization in Low-resource Settings

Automated software engineering with deep learning techniques has been comprehensively explored because of breakthroughs in code representation learning. Many code intelligence approaches have been proposed for the downstream tasks of this field in the past years, contributing to significant performance progress. Code summarization has been the central research topic among these downstream tasks because of […]
Nov, 13

pyGSL: A Graph Structure Learning Toolkit

We introduce pyGSL, a Python library that provides efficient implementations of state-of-the-art graph structure learning models along with diverse datasets to evaluate them on. The implementations are written in GPU-friendly ways, allowing one to scale to much larger network tasks. A common interface is introduced for algorithm unrolling methods, unifying implementations of recent state-of-the-art techniques […]
Nov, 13

iGniter: Interference-Aware GPU Resource Provisioning for Predictable DNN Inference in the Cloud

GPUs are essential to accelerating the latency-sensitive deep neural network (DNN) inference workloads in cloud datacenters. To fully utilize GPU resources, spatial sharing of GPUs among co-located DNN inference workloads becomes increasingly compelling. However, GPU sharing inevitably brings severe performance interference among co-located inference workloads, as motivated by an empirical measurement study of DNN inference […]
Nov, 13

Multi-GPU thermal lattice Boltzmann simulations using OpenACC and MPI

We assess the performance of the hybrid Open Accelerator (OpenACC) and Message Passing Interface (MPI) approach for multi-graphics processing units (GPUs) accelerated thermal lattice Boltzmann (LB) simulation. The OpenACC accelerates computation on a single GPU, and the MPI synchronizes the information between multiple GPUs. With a single GPU, the two-dimension (2D) simulation achieved 1.93 billion […]
Nov, 6

Enabling Data Movement and Computation Pipelining in Deep Learning Compiler

Pipelining between data loading and computation is a critical tensor program optimization for GPUs. Multi-stage pipelining across the multi-level buffer hierarchy of GPU is particularly indispensable on the latest NVIDIA Ampere GPUs to reduce resource idleness and guarantee kernel performance. Currently, people rely on libraries written by experts such as cuBLAS to access the pipelining […]
Nov, 6

Using scheduling entropy amplification in CUDA/OpenMP code to exhibit non-reproducibility issues

Rounding error or cancellation that appears with each floating-point operations, combined with the lack of control over execution order in parallel code leads to numerical issues such as numerical reproducibility. In order to enhance the possibility to discover such numerical issue, in this article we propose a simple solution base on an index interposer and […]
Nov, 6

An Open-source FPGA Library for Data Sorting

Field-programmable gate arrays (FPGAs) have garnered significant interest in research on high-performance computing because their flexibility enables the building of application-specific computation pipelines and data supply systems. In addition to the flexibility, toolchains for the development of FPGAs in OpenCL have been developed and offered by FPGA vendors that reduce the programming effort required. However, […]
Nov, 6

Apple Silicon Performance in Scientific Computing

With the release of the Apple Silicon System-on-a-Chip processors, and the impressive performance shown in general use by both the M1 and M1 Ultra, the potential use for Apple Silicon processors in scientific computing is explored. Both the M1 and M1 Ultra are compared to current state-of-the-art data-center GPUs, including an NVIDIA V100 with PCIe, […]

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