Memory access patterns of parallel scientific programs
SIGMETRICS '87 Proceedings of the 1987 ACM SIGMETRICS conference on Measurement and modeling of computer systems
Performance benefits and limitations and limitations of large NUMA multiprocessors
Performance '93 Proceedings of the 16th IFIP Working Group 7.3 international symposium on Computer performance modeling measurement and evaluation
Implementing global memory management in a workstation cluster
SOSP '95 Proceedings of the fifteenth ACM symposium on Operating systems principles
Coordinated allocation of memory and processors in multiprocessors
Proceedings of the 1996 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Benefits of speedup knowledge in memory-constrained multiprocessor scheduling
Performance Evaluation
Availability and utility of idle memory in workstation clusters
SIGMETRICS '99 Proceedings of the 1999 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Dynamic Cluster Resource Allocations for Jobs with Known and Unknown Memory Demands
IEEE Transactions on Parallel and Distributed Systems
IPPS '95 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
Packing Schemes for Gang Scheduling
IPPS '96 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
Memory Usage in the LANL CM-5 Workload
IPPS '97 Proceedings of the Job Scheduling Strategies for Parallel Processing
Performance Evaluation of Gang Scheduling for Parallel and Distributed Multiprogramming
IPPS '97 Proceedings of the Job Scheduling Strategies for Parallel Processing
Theory and Practice in Parallel Job Scheduling
IPPS '97 Proceedings of the Job Scheduling Strategies for Parallel Processing
IPPS/SPDP '98 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
Overhead Analysis of Preemptive Gang Scheduling
IPPS/SPDP '98 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
Metrics and Benchmarking for Parallel Job Scheduling
IPPS/SPDP '98 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
Improving First-Come-First-Serve Job Scheduling by Gang Scheduling
IPPS/SPDP '98 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
Lachesis: A Job Scheduler for the Cray T3E
IPPS/SPDP '98 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
Metrics for Parallel Job Scheduling and Their Convergence
JSSPP '01 Revised Papers from the 7th International Workshop on Job Scheduling Strategies for Parallel Processing
Incorporating Job Migration and Network RAM to Share Cluster Memory Resources
HPDC '00 Proceedings of the 9th IEEE International Symposium on High Performance Distributed Computing
Characterization of Backfilling Strategies for Parallel Job Scheduling
ICPPW '02 Proceedings of the 2002 International Conference on Parallel Processing Workshops
Gang Scheduling with Memory Considerations
IPDPS '00 Proceedings of the 14th International Symposium on Parallel and Distributed Processing
Improving Parallel Job Scheduling by Combining Gang Scheduling and Backfilling Techniques
IPDPS '00 Proceedings of the 14th International Symposium on Parallel and Distributed Processing
Adaptive Memory Allocations in Clusters to Handle Unexpectedly Large Data-Intensive Jobs
IEEE Transactions on Parallel and Distributed Systems
ICPP '04 Proceedings of the 2004 International Conference on Parallel Processing
Implementation of a reliable remote memory pager
ATEC '96 Proceedings of the 1996 annual conference on USENIX Annual Technical Conference
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Large scientific parallel applications demand large amounts of memory space. Current parallel computing platforms schedule jobs without fully knowing their memory requirements. This leads to uneven memory allocation in which some nodes are overloaded. This, in turn, leads to disk paging, which is extremely expensive in the context of scientific parallel computing. To solve this problem, we propose a new peer-to-peer solution called Parallel Network RAM. This approach avoids the use of disk, better utilizes available RAM resources, and will allow larger problems to be solved while reducing the computational, communication, and synchronization overhead typically involved in parallel applications. We proposed several different Parallel Network RAM designs and evaluated the performance of each under different conditions. We discovered that different designs are appropriate in different situations.