Aug, 28

Exploring Thread Coarsening on FPGA

Over the past few years, there has been an increased interest in including FPGAs in data centers and high-performance computing clusters along with GPUs and other accelerators. As a result, it has become increasingly important to have a unified, high-level programming interface for CPUs, GPUs and FPGAs. This has led to the development of compiler […]
Aug, 21

BenchPress: A Deep Active Benchmark Generator

We develop BenchPress, the first ML benchmark generator for compilers that is steerable within feature space representations of source code. BenchPress synthesizes compiling functions by adding new code in any part of an empty or existing sequence by jointly observing its left and right context, achieving excellent compilation rate. BenchPress steers benchmark generation towards desired […]
Aug, 21

Performance analysis of matrix-free conjugate gradient kernels using SYCL

We examine the performance of matrix-free SYCL implementations of the conjugate gradient method for solving sparse linear systems of equations. Performance is tested on an NVIDIA A100-80GB device and a dual socket Intel Ice Lake CPU node using different SYCL implementations, and compared to CUDA BLAS (cuBLAS) implementations on the A100 GPU and MKL implementations […]
Aug, 21

Zeus: Understanding and Optimizing GPU Energy Consumption of DNN Training

Training deep neural networks (DNNs) is becoming more and more resource- and energy-intensive every year. Unfortunately, existing works primarily focus on optimizing DNN training for faster completion, often without considering the impact on energy efficiency. In this paper, we observe that common practices to improve training performance can often lead to inefficient energy usage. More […]
Aug, 21

Optimization of GPU workloads using natural language processing based on deep learning techniques

Setting program parameters is challenging due to the abstract relationship between hardware and software. Automatic optimization algorithms that are accurate are required to cope with the complexity and variety of current hardware and software. Autotuning has always relied on time-consuming trial and error approaches. Machine learning (ML) and Natural Language Processing (NLP) has flourished over […]
Aug, 21

LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale

Large language models have been widely adopted but require significant GPU memory for inference. We develop a procedure for Int8 matrix multiplication for feed-forward and attention projection layers in transformers, which cut the memory needed for inference by half while retaining full precision performance. With our method, a 175B parameter 16/32-bit checkpoint can be loaded, […]
Aug, 7

A Container-Based Workflow for Distributed Training of Deep Learning Algorithms in HPC Clusters

Deep learning has been postulated as a solution for numerous problems in different branches of science. Given the resource-intensive nature of these models, they often need to be executed on specialized hardware such graphical processing units (GPUs) in a distributed manner. In the academic field, researchers get access to this kind of resources through High […]
Aug, 7

Deep Learning Approaches to Source Code Analysis for Optimization of Heterogeneous Systems: Recent Results, Challenges and Opportunities

To cope with the increasing complexity of digital systems programming, deep learning techniques have recently been proposed to enhance software deployment by analysing source code for different purposes, ranging from performance and energy improvement to debugging and security assessment. As embedded platforms for cyber-physical systems are characterised by increasing heterogeneity and parallelism, one of the […]
Aug, 7

COX: Exposing CUDA Warp-Level Functions to CPUs

As CUDA becomes the de facto programming language among data parallel applications such as high-performance computing or machine learning applications, running CUDA on other platforms becomes a compelling option. Although several efforts have attempted to support CUDA on devices other than NVIDIA GPUs, due to extra steps in the translation, the support is always a […]
Aug, 7

Design and Implementation of ShenWei Universal C/C++

The ShenWei many-core series processors powering multiple cutting-edge supercomputers are equipped with their unique on-chip heterogeneous architecture. They have long required programmers to write separate codes for the control part on Management Processing Element (MPE) and accelerated part on Compute Processing Element (CPE), which is similar to open standards like OpenCL. Such a programming model […]
Aug, 7

Real-Time High-Performance Computing for Embedded Control Systems

Critical real-time systems include a wide spectrum of computer systems whose correct behavior is dictated not only by correct functionality but also by their timely execution with respect to predefined deadlines. The increasing demand for higher performance in these systems has led the industry to recently include embedded Graphics Processing Units (GPUs), mainly for machine […]
Jul, 24

Demystifying Dependency Bugs in Deep Learning Stack

Recent breakthroughs in deep learning (DL) techniques have stimulated significant growth in developing DL-enabled applications. These DL applications, built upon a heterogeneous and complex DL stack (e.g., Nvidia GPU, Linux, CUDA driver, Python runtime, and TensorFlow), are subject to software and hardware dependencies across the DL stack. A persistent challenge in dependency management across the […]

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