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It is our great pleasure to welcome you to the 13th annual ACM/SPEC International Conference on Performance Engineering (ICPE)!
Planning for ICPE'22 has started early in 2021, with a landmark decision by the steering committee to hold the conference in China, for the first time in the conference's history. Due to the ongoing COVID-19 pandemic, a decision was made to strive for a hybrid conference, with local sessions in Beijing enriched by remote participants from all around the world. However, as the delta variant of COVID was followed by omicron, and border and travel restrictions around the globe intensified rather than being removed, we made the difficult decision to move to a fully virtual ICPE for the third year in a row.
Despite this, we hope that we were able to prepare a program that is no less exciting than previous iterations of ICPE. This year, we extend a warm welcome to three excellent keynote speakers, covering a range of industrial and academic topics - Ivona Brandic (TU Vienna), John Wilkes (Google), and Longxiang Li (Inspur). Following ICPE tradition, the technical program will consist of a healthy mix of academic and industrial contributions - nine full research paper presentations, four short research paper presentations, as well as nine presentations in the industry and experience track. Additionally, the program will offer workshops, tutorials, a work-in-progress track, as well as a demo/poster track. Finally, and for the first time, we have also included a data challenge, where students and researchers were able to study a large dataset of real-life performance traces donated by MongoDB Inc.
Proceeding Downloads
Data Science Driven Methods for Sustainable and Failure Tolerant Edge Systems
Nowadays we experience a paradigm shift in our society, where every item around us is becoming a computer facilitating life-changing applications like self-driving cars, tele-medicine, precision agriculture or virtual reality. On one hand, for the ...
Performance Optimization of HPC Applications in Large-Scale Cluster Systems
In modern HPC clusters, the performance of an application is a combination of several aspects. To successfully improve the application performance, all performance aspects should be analyzed and optimized. In particular, as modern CPUs contain more and ...
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LongTale: Toward Automatic Performance Anomaly Explanation in Microservices
Performance troubleshooting is notoriously difficult for distributed microservices-based applications. A typical root-cause diagnosis for performance anomaly by an analyst starts by narrowing down the scope of slow services, investigates into high-level ...
An Empirical Study of Service Mesh Traffic Management Policies for Microservices
A microservice architecture features hundreds or even thousands of small loosely coupled services with multiple instances. Because microservice performance depends on many factors including the workload, inter-service traffic management is complex in ...
Best Practices for HPC Workloads on Public Cloud Platforms: A Guide for Computational Scientists to Use Public Cloud for HPC Workloads
HPC (high performance computing) applications come with a variety of requirements for computation, communication, and storage; and many of these requirements can be met with commodity technology available in public clouds. In this article, we report on ...
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NVMe Virtualization for Cloud Virtual Machines
Public clouds are rapidly moving to support Non-Volatile Memory Express (NVMe) based storage to meet the ever-increasing I/O throughput and latency demands of modern workloads. They provide NVMe storage through virtual machines (VMs) where multiple VMs ...
Exploring the Use of Novel Spatial Accelerators in Scientific Applications
Driven by the need to find alternative accelerators which can viably replace graphics processing units (GPUs) in next-generation Supercomputing systems, this paper proposes a methodology to enable agile application/hardware co-design. The application-...
Extending SYCL's Programming Paradigm with Tensor-based SIMD Abstractions
Heterogeneous computing has emerged as an important method for supporting more than one kind of processors or accelerators in a program. There is generally a trade off between source code portability and device performance for heterogeneous programming. ...
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Oversubscribing GPU Unified Virtual Memory: Implications and Suggestions
Recent GPU architectures support unified virtual memory (UVM), which offers great opportunities to solve larger problems by memory oversubscription. Although some studies are concerned over the performance degradation under UVM oversubscription, the ...
Isolating GPU Architectural Features Using Parallelism-Aware Microbenchmarks
GPUs develop at a rapid pace, with new architectures emerging every 12 to 18 months. Every new GPU architecture introduces new features, expecting to improve on previous generations. However, the impact of these changes on the performance of GPGPU ...
Same, Same, but Dissimilar: Exploring Measurements for Workload Time-series Similarity
- Mark Leznik,
- Johannes Grohmann,
- Nina Kliche,
- André Bauer,
- Daniel Seybold,
- Simon Eismann,
- Samuel Kounev,
- Jörg Domaschka
Benchmarking is a core element in the toolbox of most systems researchers and is used for analyzing, comparing, and validating complex systems. In the quest for reliable benchmark results, a consensus has formed that a significant experiment must be ...
Studying the Performance Risks of Upgrading Docker Hub Images: A Case Study of WordPress
The Docker Hub repository contains Docker images of applications, which allow users to do in-place upgrades to benefit from the latest released features and security patches. However, prior work showed that upgrading a Docker image not only changes the ...
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Why Is It Not Solved Yet?: Challenges for Production-Ready Autoscaling
Autoscaling is a task of major importance in the cloud computing domain as it directly affects both operating costs and customer experience. Although there has been active research in this area for over ten years now, there is still a significant gap ...
Evaluating the Scalability and Elasticity of Function as a Service Platform
Function as a Service (FaaS) is a new software technology with promising features such as automated resource management and auto-scaling. Since these operational aspects are transparent, software engineers may not fully understand the scaling ...
Machine Learning based Interference Modelling in Cloud-Native Applications
Cloud-native applications are often composed of lightweight containers and conform to the microservice architecture. Cloud providers offer platforms for container hosting and orchestration. These platforms reduce the level of support required from the ...
Performance Model and Profile Guided Design of a High-Performance Session Based Recommendation Engine
Session-based recommendation (SBR) systems are widely used in transactional systems to make personalized recommendations to the end-user. In online retail systems, recommendations-based decisions need to be made at a very high rate especially during ...
The Cost of Reinforcement Learning for Game Engines: The AZ-Hive Case-study
Although utilising computers to play board games has been a topic of research for many decades, the recent rapid developments in the field of reinforcement learning - like AlphaZero and variants - brought unprecedented progress in games such as chess ...
Alternating Blind Identification of Power Sources for Mobile SoCs
The need for faster Systems on Chip (SoCs) has accelerated scaling trends, leading to a considerable power density increase and raising critical power and thermal challenges. The ability to measure power consumption of different hardware units is ...
Memory Performance of AMD EPYC Rome and Intel Cascade Lake SP Server Processors
Modern processors, in particular within the server segment, integrate more cores with each generation. This increases their complexity in general, and that of the memory hierarchy in particular. Software executed on such processors can suffer from ...
Near-Storage Processing for Solid State Drive Based Recommendation Inference with SmartSSDs®
- Mohammadreza Soltaniyeh,
- Veronica Lagrange Moutinho Dos Reis,
- Matt Bryson,
- Xuebin Yao,
- Richard P. Martin,
- Santosh Nagarakatte
Deep learning-based recommendation systems are extensively deployed in numerous internet services, including social media, entertainment services, and search engines, to provide users with the most relevant and personalized content. Production scale ...
HLS_Profiler: Non-Intrusive Profiling Tool for HLS based Applications
The High-Level Synthesis (HLS) tools aid in simplified and faster design development without familiarity with Hardware Description Language (HDL) and Register Transfer Logic (RTL) design flow. However, it is not straight forward to associate every line ...
A Mixed PS-FCFS Policy for CPU Intensive Workloads
Round robin (RR) is a widely adopted scheduling policy in modern computer systems. The scheduler handles the concurrency by alternating the run processes in such a way that they can use the processor continuously for at most a quantum of time. When the ...
A Stochastic Extension of Stateflow
Although commonly used in industry, a major drawback of Stateflow is that it lacks support for stochastic properties; properties that are often needed to build accurate models of real-world systems. In order to solve this problem, as the first ...
Sampling-based Label Propagation for Balanced Graph Partitioning
In this experience paper, we present new sampling-based algorithms for balanced graph partitioning based on the Label Propagation (LP) approach. The purpose is to define new heuristics to extend the multi-objective and scalable Balanced GRAph ...
Index Terms
- Proceedings of the 2022 ACM/SPEC on International Conference on Performance Engineering