high performance computing on graphics processing units: hgpu.org

Heterogeneous computing systems provide high performance and energy efficiency. However, to optimally utilize such systems, solutions that distribute the work across host CPUs and accelerating devices are needed. In this paper, we present a performance and energy aware approach that combines AI planning heuristics for parameter space exploration with a machine learning model for performance […]

Programming efficiently heterogeneous systems is a major challenge, due to the complexity of their architectures. Intel oneAPI, a new and powerful standards-based unified programming model, built on top of SYCL, addresses these issues. In this paper, oneAPI is provided with co-execution strategies to run the same kernel between different devices, enabling the exploitation of static […]

The heterogeneous computing paradigm represents a real programming challenge due to the proliferation of devices with different hardware characteristics. Recently Intel introduced oneAPI, a new programming environment that allows code developed in DPC++ to be run on different devices such as CPUs, GPUs, FPGAs, among others. This paper presents our first experiences in porting two […]

Graph analytics represent an important application domain widely used in many fields such as web graphs, social networks, and Bayesian networks. The sheer size of the graph data sets combined with the irregular nature of the underlying problem pose a significant challenge for performance, scalability, and power efficiency of graph processing. With the exponential growth […]

Empirical Dynamic Modeling (EDM) is a state-of-the-art non-linear time-series analysis framework. Despite its wide applicability, EDM was not scalable to large datasets due to its expensive computational cost. To overcome this obstacle, researchers have attempted and succeeded in accelerating EDM from both algorithmic and implementational aspects. In previous work, we developed a massively parallel implementation […]

Within Transformer, self-attention is the key module to learn powerful context-aware representations. However, self-attention suffers from quadratic memory requirements with respect to the sequence length, which limits us to process longer sequence on GPU. In this work, we propose sequence parallelism, a memory efficient parallelism method to help us break input sequence length limitation and […]

Recently, deep learning has been an area of intense researching. However, as a kind of computing intensive task, deep learning highly relies on the the scale of the GPU memory, which is usually expensive and scarce. Although there are some extensive works have been proposed for dynamic GPU memory management, they are hard to be […]

Error-bounded lossy compression is a critical technique for significantly reducing scientific data volumes. With ever-emerging heterogeneous HPC architecture, GPU-accelerated error-bounded compressors (such as cuSZ and cuZFP) have been developed. However, they suffer from either low performance or low compression ratios. To this end, we propose cuSZ(x) to target both high compression ratio and throughput. We […]

Although Graphics Processing Units (GPUs) have become pervasive for data-parallel workloads, the efficient exploitation of their tiered memory hierarchy requires explicit programming. The efficient utilization of different GPU memory tiers can yield higher performance at the expense of programmability since developers must have extended knowledge of the architectural details in order to utilize them. In […]

Image denoising is one of the most critical problems in mobile photo processing. While many solutions have been proposed for this task, they are usually working with synthetic data and are too computationally expensive to run on mobile devices. To address this problem, we introduce the first Mobile AI challenge, where the target is to […]

Support for lower precision computation is becoming more common in accelerator hardware due to lower power usage, reduced data movement and increased computational performance. However, computational science and engineering (CSE) problems require double precision accuracy in several domains. This conflict between hardware trends and application needs has resulted in a need for multiprecision strategies at […]

Modern deep learning workloads run on distributed hardware and are difficult to optimize — data, model, and pipeline parallelism require a developer to thoughtfully restructure their workload around optimized computation and communication kernels in libraries such as cuBLAS and NCCL. The logical separation between computation and communication leaves performance on the table with missed optimization […]