High Performance Cluster Computing: Architectures and Systems
By Rajkumar Buyya. 1999
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Rajkumar Buyya
- David Abramson
- Mark A. Baker
- Jonathan Giddy
- Jia Yu
- Domenico Laforenza
- Miron Livny
- Marc-André Zöller
Citations in the corpus
- Benchmark and Survey of Automated Machine Learning Frameworks (2019, background): “A common strategy for solving a computational heavy problem is parallelization on multiple cores or within a cluster (e.g., Buyya, 1999; Dean & Ghemawat, 2008). scikit-learn (Pedregosa et al., 2011), which is used by most evaluated frameworks (see Section 8), already implements optimizations to…”
- Survey on Automated Machine Learning (2019, background): “A common strategy for solving a computational heavy problem is parallelization on multiple cores or within a cluster, e.g., (Buyya, 1999; Dean and Ghemawat, 2008).”
- Condor and the Grid (2003, background): “While providing functionality similar to that of a more traditional batch queueing system, Condor's novel architecture and unique mechanisms allow it to perform well in environments where a traditional RMS is weak — areas such as sustained high-throughput computing and Copyright c 2002 John Wiley & ”
- Grids and Grid technologies for wide‐area distributed computing (2002, background): “To build a Grid requires the development and deployment of a number of low-level services, including those for: resource discovery, scheduling configuration management, security, and payment mechanisms in an open environment [1, 2, 6]; and high-level services for application composition, execution m”
- Nimrod/G: an architecture for a resource management and scheduling system in a global computational grid (2000, background): “This technology opportunity leads to the possibility of using networks of computers as a single, unified computing resource, popularly called cluster computing [7].”
- A Survey on Distributed Machine Learning (2019, background): “Communication is an important contributor to dening the performance and scalability of distributed processing [27].”