high performance computing on graphics processing units: hgpu.org

Currently, offloading to accelerators requires users to identify which regions are to be executed on the device, what memory needs to be transferred, and how synchronization is to be resolved. On top of these manual tasks, many standard (C/C++ library) functions, such as file I/O or memory manipulation, cannot be directly executed on the device […]

Large transformer models display promising performance on a wide range of natural language processing (NLP) tasks. Although the AI community has expanded the model scale to the trillion parameter level, the practical deployment of 10-100 billion parameter models is still uncertain due to the latency, throughput, and memory constraints. In this paper, we proposed EnergonAI […]

Sparse compiler is a promising solution for sparse tensor algebra optimization. In compiler implementation, reduction in sparse-dense hybrid algebra plays a key role in performance. Though GPU provides various reduction semantics that can better utilize the parallel computing and memory bandwidth capacity, the central question is: how to elevate the flexible reduction semantics to sparse […]

Supercomputers worldwide provide the necessary infrastructure for groundbreaking research. However, most supercomputers are not designed equally due to different desired figure of merit, which is derived from the computational bounds of the targeted scientific applications’ portfolio. In turn, the design of such computers becomes an optimization process that strives to achieve the best performances possible […]

Recently, CPU-GPU heterogeneous parallelism has brought transformational performance milestones to static timing analysis (STA) algorithms. As the computing ecosystem continues to proliferate, performance portability has emerged as a new challenge when deploying the result to diverse heterogeneous computing platforms. Specifically, the optimal code written on a CPU-GPU architecture may not be optimal for other CPUGPU […]

We present a dynamically Growable GPU array (GGArray) fully implemented in GPU that does not require synchronization with the host. The idea is to improve the programming of GPU applications that require dynamic memory, by offering a structure that does not require pre-allocating GPU VRAM for the worst case scenario. The GGArray is based on […]

The continuous technology push on the semiconductor industry has led to the development of several alternate architectures for efficient computing. Field-Programmable Gate Arrays (FPGAs) and Graphics Processing Units (GPUs) are examples of devices used to accelerate applications. FPGAs are able to provide massive parallelism for suitable tasks when properly programmed. However, designing for FPGA is […]

Nowadays, we are living in an era of extreme device heterogeneity. Despite the high variety of conventional CPU architectures, accelerator devices, such as GPUs and FPGAs, also appear in the foreground exploding the pool of available solutions to execute applications. However, choosing the appropriate device per application needs is an extremely challenging task due to […]

Achieving high performance for GPU codes requires developers to have significant knowledge in parallel programming and GPU architectures, and in-depth understanding of the application. This combination makes it challenging to find performance optimizations for GPU-based applications, especially in scientific computing. This paper shows that significant speedups can be achieved on two quite different scientific workloads […]

In an era of hardware diversity, the management of applications’ allocated memory is a complex task that can have significant performance repercussions. Non-uniformity in the memory hierarchy, along with heterogeneity and asymmetry of chip designs, make the costs of memory accesses unpredictable if the allocated memory is not managed carefully. Poor memory allocation and placement […]

Building efficient concurrent data structures that scale to the level of GPU parallelism is a challenging problem. Solutions designed for the CPU do not scale to the thousands of cores that modern GPUs offer. In this dissertation, we show how to efficiently build a lock-based B-Tree on the GPU; then, we show how to extend […]

Graphics processing units (GPUs) are now considered the leading hardware to accelerate general-purpose workloads such as AI, data analytics, and HPC. Over the last decade, researchers have focused on demystifying and evaluating the microarchitecture features of various GPU architectures beyond what vendors reveal. This line of work is necessary to understand the hardware better and […]