Related papers: Evaluating the Overhead of the Performance Profile…
Microservice architectures and design patterns enhance the development of large-scale applications by promoting flexibility. Industrial practitioners perceive the importance of applying architectural patterns but they struggle to quantify…
This report evaluates the performance impact of enabling Trusted Execution Environments (TEE) on NVIDIA Hopper GPUs for large language model (LLM) inference tasks. We benchmark the overhead introduced by TEE mode across various LLMs and…
Modern cloud-native applications built on microservice architectures present unprecedented challenges for system monitoring and alerting. Site Reliability Engineers (SREs) face the daunting challenge of defining effective monitoring…
This project aims to explore the process of deploying Machine learning models on Kubernetes using an open-source tool called Kubeflow [1] - an end-to-end ML Stack orchestration toolkit. We create end-to-end Machine Learning models on…
Embedded Systems combine one or more processor cores with dedicated logic running on an ASIC or FPGA to meet design goals at reasonable cost. It is achieved by profiling the application with variety of aspects like performance, memory…
Transformers have revolutionized AI in natural language processing and computer vision, but their large computation and memory demands pose major challenges for hardware acceleration. In practice, end-to-end throughput is often limited by…
These days more companies are shifting towards using cloud environments to provide their services to their client. While it is easy to set up a cloud environment, it is equally important to monitor the system's runtime behaviour and…
This paper is devoted to the development of highly efficient kernels performing vector operations relevant in linear system solvers. In particular, we focus on the low arithmetic intensity operations (i.e., streaming operations) performed…
In order to compare and benchmark the mathematical software, the performance profiles have been introduced [1]. However, it has been proved that the algorithm is not flawless. The main issue with the performance profile is that it may rank…
As AI-assisted development tools proliferate, developers face a growing challenge: understanding the cost, quality, and behavioral patterns of AI interactions across their workflow. We present a unified approach to AI observability for…
GPUs are the dominant platform for many important applications today including deep learning, accelerated computing, and scientific simulation. However, as the complexity of both applications and hardware increases, GPU chip manufacturers…
Kubernetes has emerged as a leading open-source platform for container orchestration, allowing organizations to efficiently manage and deploy containerized applications at scale. This paper investigates the performance of four Kubernetes…
Different bitstring representations can yield varying computational performance. This work compares three bitstring implementations in C++: std::bitset, boost::dynamic_bitset, and a custom direct implementation. Their performance is…
A processor's memory hierarchy has a major impact on the performance of running code. However, computing platforms, where the actual hardware characteristics are hidden from both the end user and the tools that mediate execution, such as a…
Since the advent of parallel algorithms in the C++17 Standard Template Library (STL), the STL has become a viable framework for creating performance-portable applications. Given multiple existing implementations of the parallel algorithms,…
Privacy protection has become an increasing concern in modern machine learning applications. Privacy-preserving machine learning (PPML) has attracted growing research attention, with approaches such as secure multiparty computation (MPC)…
The impressive performance of transformer models has sparked the deployment of intelligent applications on resource-constrained edge devices. However, ensuring high-quality service for real-time edge systems is a significant challenge due…
In the current high-performance and embedded computing era, full-stack energy-centric design is paramount. Use cases require increasingly high performance at an affordable power budget, often under real-time constraints. Extreme…
Hardware performance monitoring (HPM) is a crucial ingredient of performance analysis tools. While there are interfaces like LIKWID, PAPI or the kernel interface perf\_event which provide HPM access with some additional features, many…
Edge computing has emerged as a popular paradigm for running latency-sensitive applications due to its ability to offer lower network latencies to end-users. In this paper, we argue that despite its lower network latency, the…