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Posts

Mar, 18

Cost-Effective Methodology for Complex Tuning Searches in HPC: Navigating Interdependencies and Dimensionality

Tuning searches are pivotal in High-Performance Computing (HPC), addressing complex optimization challenges in computational applications. The complexity arises not only from finely tuning parameters within routines but also potential interdependencies among them, rendering traditional optimization methods inefficient. Instead of scrutinizing interdependencies among parameters and routines, practitioners often face the dilemma of conducting independent tuning searches […]
Mar, 18

Fast Truncated SVD of Sparse and Dense Matrices on Graphics Processors

We investigate the solution of low-rank matrix approximation problems using the truncated SVD. For this purpose, we develop and optimize GPU implementations for the randomized SVD and a blocked variant of the Lanczos approach. Our work takes advantage of the fact that the two methods are composed of very similar linear algebra building blocks, which […]
Mar, 18

MUPPET: Optimizing Performance in OpenMP via Mutation Testing

Performance optimization continues to be a challenge in modern HPC software. Existing performance optimization techniques, including profiling-based and auto-tuning techniques, fail to indicate program modifications at the source level thus preventing their portability across compilers. This paper describes Muppet, a new approach that identifies program modifications called mutations aimed at improving program performance. Muppet’s mutations […]
Mar, 18

SYCL in the edge: performance and energy evaluation for heterogeneous acceleration

Edge computing is essential to handle increasing data volumes and processing capacities. It provides real-time and secure data processing near data sources, like smart devices, alleviating cloud computing energy use, and saving network bandwidth. Specialized accelerators, like GPUs and FPGAs, are vital for low-latency edge computing but the requirements to customized code for different hardware […]
Mar, 18

Predicting GPUDirect Benefits for HPC Workloads

Graphics processing units (GPUs) are becoming increasingly popular in modern HPC systems. Hardware for data movement to and from GPUs such as NVLink and GPUDirect has reduced latencies, increased throughput, and eliminated redundant copies. In this work, we use discrete event simulations to explore the impact of different communication paradigms on the messaging performance of […]
Mar, 10

FTTN: Feature-Targeted Testing for Numerical Properties of NVIDIA & AMD Matrix Accelerators

NVIDIA Tensor Cores and AMD Matrix Cores (together called Matrix Accelerators) are of growing interest in high-performance computing and machine learning owing to their high performance. Unfortunately, their numerical behaviors are not publicly documented, including the number of extra precision bits maintained, the accumulation order of addition, and predictable subnormal number handling during computations. This […]
Mar, 10

Distributed OpenMP Offloading of OpenMC on Intel GPU MAX Accelerators

Monte Carlo (MC) simulations play a pivotal role in diverse scientific and engineering domains, with applications ranging from nuclear physics to materials science. Harnessing the computational power of high-performance computing (HPC) systems, especially Graphics Processing Units (GPUs), has become essential for accelerating MC simulations. This paper focuses on the adaptation and optimization of the OpenMC […]
Mar, 10

Hybrid quantum programming with PennyLane Lightning on HPC platforms

We introduce PennyLane’s Lightning suite, a collection of high-performance state-vector simulators targeting CPU, GPU, and HPC-native architectures and workloads. Quantum applications such as QAOA, VQE, and synthetic workloads are implemented to demonstrate the supported classical computing architectures and showcase the scale of problems that can be simulated using our tooling. We benchmark the performance of […]
Mar, 10

SYCL-Bench 2020: Benchmarking SYCL 2020 on AMD, Intel, and NVIDIA GPUs

Today, the SYCL standard represents the most advanced programming model for heterogeneous computing, delivering both productivity, portability, and performance in pure C++17. SYCL 2020, in particular, represents a major enhancement that pushes the boundaries of heterogeneous programming by introducing a number of new features. As the new features are implemented by existing compilers, it becomes […]
Mar, 10

Parallel Implementation of Lightweight Secure Hash Algorithm on CPU and GPU Environments

Currently, cryptographic hash functions are widely used in various applications, including message authentication codes, cryptographic random generators, digital signatures, key derivation functions, and post-quantum algorithms. Notably, they play a vital role in establishing secure communication between servers and clients. Specifically, servers often need to compute a large number of hash functions simultaneously to provide smooth […]
Mar, 3

Using AI libraries for Incompressible Computational Fluid Dynamics

Recently, there has been a huge effort focused on developing highly efficient open source libraries to perform Artificial Intelligence (AI) related computations on different computer architectures (for example, CPUs, GPUs and new AI processors). This has not only made the algorithms based on these libraries highly efficient and portable between different architectures, but also has […]
Mar, 3

Sustainable Supercomputing for AI: GPU Power Capping at HPC Scale

As research and deployment of AI grows, the computational burden to support and sustain its progress inevitably does too. To train or fine-tune state-of-the-art models in NLP, computer vision, etc., some form of AI hardware acceleration is virtually a requirement. Recent large language models require considerable resources to train and deploy, resulting in significant energy […]

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