Sökning: "Data-intensive computing"
Visar resultat 1 - 5 av 9 uppsatser innehållade orden Data-intensive computing.
1. Investigating programming language support for fault-tolerance
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Dataflow systems have become the norm for developing data-intensive computing applications. These systems provide transparent scalability and fault tolerance. For fault tolerance, many dataflow-system adopt a snapshotting approach which persists the state of an operator once it has received a snapshot marker on all its input channels. LÄS MER
2. Low-power Acceleration of Convolutional Neural Networks using Near Memory Computing on a RISC-V SoC
Master-uppsats, Lunds universitet/Institutionen för elektro- och informationsteknikSammanfattning : The recent peak in interest for artificial intelligence, partly fueled by language models such as ChatGPT, is pushing the demand for machine learning and data processing in everyday applications, such as self-driving cars, where low latency is crucial and typically achieved through edge computing. The vast amount of data processing required intensifies the existing performance bottleneck of the data movement. LÄS MER
3. An I/O-aware scheduler for containerized data-intensive HPC tasks in Kubernetes-based heterogeneous clusters
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Cloud-native is a new computing paradigm that takes advantage of key characteristics of cloud computing, where applications are packaged as containers. The lifecycle of containerized applications is typically managed by container orchestration tools such as Kubernetes, the most popular container orchestration system that automates the containers’ deployment, maintenance, and scaling. LÄS MER
4. AXI-PACK : Near-memory Bus Packing for Bandwidth-Efficient Irregular Workloads
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : General propose processor (GPP) are demanded high performance in dataintensive applications, such as deep learning, high performance computation (HPC), where algorithm kernels like GEMM (general matrix-matrix multiply) and SPMV (sparse matrix-vector multiply) kernels are intensively used. The performance of these data-intensive applications are bounded with memory bandwidth, which is limited by computing & memory access coupling and memory wall effect. LÄS MER
5. Project based multi-tenant managed RStudio on Kubernetes for Hopsworks
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : In order to fully benefit from cloud computing, services are designed following the “multi-tenant” architectural model which is aimed at maximizing resource sharing among users. However, multi-tenancy introduces challenges of security, performance isolation, scaling and customization. LÄS MER
