Browsing by Author "Wang, HH"

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Classifying and alleviating the communication overheads in matrix computations on large-scale NUMA multiprocessors
(1998-12-01)
Wang, YM
;
Wang, HH
;
Chang, RC
;
資訊工程學系
;
Department of Computer Science
Large-scale, shared-memory multiprocessors have non-uniform memory access (NUMA) costs. The high communication cost dominates the source of matrix computations' execution. Memory contention and remote memory access are two major communication overheads on large-scale NUMA multiprocessors. However, previous experiments and discussions focus either on reducing the number of remote memory accesses or on alleviating memory contention overhead. In this paper, we propose a simple but effective processor allocation policy, called rectangular processor allocation, to alleviate both overheads at the same time. The policy divides the matrix elements into a certain number of rectangular blocks, and assigns each processor to compute the results of one rectangular block. This methodology may reduce a lot of unnecessary memory accesses to the memory modules. After running many matrix computations under a realistic memory system simulator, we confirmed that at least one-fourth of the communication overhead map be reduced. Therefore, we conclude that rectangular processor allocation policy performs better than other popular policies, and that the combination of rectangular processor allocation policy with software interleaving data allocation policy is a better choice to alleviate communication overhead. (C) 1998 Elsevier Science Inc. All rights reserved.
Classifying and alleviating the communication overheads in matrix computations on large-scale NUMA multiprocessors
(1998-12-01)
Wang, YM
;
Wang, HH
;
Chang, RC
;
資訊工程學系
;
Department of Computer Science
Large-scale, shared-memory multiprocessors have non-uniform memory access (NUMA) costs. The high communication cost dominates the source of matrix computations' execution. Memory contention and remote memory access are two major communication overheads on large-scale NUMA multiprocessors. However, previous experiments and discussions focus either on reducing the number of remote memory accesses or on alleviating memory contention overhead. In this paper, we propose a simple but effective processor allocation policy, called rectangular processor allocation, to alleviate both overheads at the same time. The policy divides the matrix elements into a certain number of rectangular blocks, and assigns each processor to compute the results of one rectangular block. This methodology may reduce a lot of unnecessary memory accesses to the memory modules. After running many matrix computations under a realistic memory system simulator, we confirmed that at least one-fourth of the communication overhead map be reduced. Therefore, we conclude that rectangular processor allocation policy performs better than other popular policies, and that the combination of rectangular processor allocation policy with software interleaving data allocation policy is a better choice to alleviate communication overhead. (C) 1998 Elsevier Science Inc. All rights reserved.
Clustered affinity scheduling on large-scale NUMA multiprocessors
(1997-10-01)
Wang, YM
;
Wang, HH
;
Chang, RC
;
資訊工程學系
;
Department of Computer Science
Modern shared-memory multiprocessors have high and non-uniform memory access (NUMA) costs. The communication cost gradually dominates the source of parallel applications' execution. Algorithms based on affinity, like affinity scheduling algorithm (AFS), perform better than dynamic algorithms, such as guided self-scheduling (GSS) and trapezoid self-scheduling (TSS). However, as the number of processors increases, AFS suffers heavy overheads for migrating workload. The overheads include remote reads to the queues for the indices information, synchronous writes to the queues for migrating iterations, and the time in loading data into cache. In this paper, we propose a new loop scheduling algorithm, clustered affinity scheduling (CAFS), to improve affinity scheduling algorithm. We distribute the processors into several clusters, and cluster-based migrations are carried on when imbalance occurs. We confirm our idea by running many applications under a realistic hierarchy memory simulator. Our results show that CAFS reduces at least 1/3 of both remote reads and synchronous writes to the queues under most applications. CAFS also improves the cache hit ratios, and balances the workload. Therefore, we conclude that under large NUMA multiprocessor, CAFS is a better choice among loop scheduling algorithms. (C) 1997 Elsevier Science Inc.
Complementary carbon nanotube-gated carbon nanotube thin-film transistor
(2006-02-27)
Chen, BH
;
Lin, HC
;
Huang, TY
;
Wei, JH
;
Wang, HH
We introduce, a complementary carbon nanotube (CNT)-gated CNT thin-film field effect transistor (FET). By using two perpendicularly crossed single-wall CNT (SWNT) bundles as the gate and the channel interchangeably, a sub-50 nm complementary CNT-FET is demonstrated. It is found that the new CNT-FET shows acceptable FET characteristics by interchanging the roles of the gate and the channel. The unique dual functionality of the device will open up a new possibility and flexibility in the design of future complementary CNT electronic circuits. (c) 2006 American Institute of Physics.
Hierarchical loop scheduling for clustered NUMA machines
(2000-12-01)
Wang, YM
;
Wang, HH
;
Chang, RC
;
資訊工程學系
;
Department of Computer Science
Loop scheduling is an important issue in the development of high performance multiprocessors. As modern multiprocessors have high and non-uniform memory access (NUMA) costs, the communication costs dominate the execution of parallel programs. Previous affinity algorithms perform better than dynamic algorithms under non-clustered NUMA multiprocessors, but they suffer heavy overheads when migrating work load under clustered NUMA machines. In this paper, we propose a new loop scheduling policy, hierarchical policy, to improve Various affinity scheduling algorithms (AFSs) for clustered NUMA machines. We cyclically distribute the iteration chunks to clusters. When imbalance occurs, the migration of iterations is carried on hierarchically. We use hierarchical policy to improve AFS and modified AFS (MAFS), and we call them Hierarchical AFS (HAFS) and Hierarchical MAFS (HMAFS), respectively. AFS uses a deterministic assignment policy to assign repeated executions of loop iteration to the same processor. MAFS modifies the migration policy of AFS, and reduces the number of synchronization operations. We confirm our idea by running many applications under a clustered NUMA simulator. Our experimental result shows that hierarchical policy reduces the inter-cluster remote memory accesses, decreases the locks to the queues, and effectively balances the work load. We also show that HMAFS is the best choice among these algorithms in most cases. (C) 2000 Elsevier Science Inc. All rights reserved.