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Efficient LowContention Parallel Algorithms
 the 1994 ACM Symp. on Parallel Algorithms and Architectures
, 1994
"... The queueread, queuewrite (qrqw) parallel random access machine (pram) model permits concurrent reading and writing to shared memory locations, but at a cost proportional to the number of readers/writers to any one memory location in a given step. The qrqw pram model reflects the contention prope ..."
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Cited by 31 (12 self)
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The queueread, queuewrite (qrqw) parallel random access machine (pram) model permits concurrent reading and writing to shared memory locations, but at a cost proportional to the number of readers/writers to any one memory location in a given step. The qrqw pram model reflects the contention properties of most commercially available parallel machines more accurately than either the wellstudied crcw pram or erew pram models, and can be efficiently emulated with only logarithmic slowdown on hypercubetype noncombining networks. This paper describes fast, lowcontention, workoptimal, randomized qrqw pram algorithms for the fundamental problems of load balancing, multiple compaction, generating a random permutation, parallel hashing, and distributive sorting. These logarithmic or sublogarithmic time algorithms considerably improve upon the best known erew pram algorithms for these problems, while avoiding the highcontention steps typical of crcw pram algorithms. An illustrative expe...
Realtime parallel hashing on the gpu
 In ACM SIGGRAPH Asia 2009 papers, SIGGRAPH ’09
, 2009
"... Figure 1: Overview of our construction for a voxelized Lucy model, colored by mapping x, y, and z coordinates to red, green, and blue respectively (far left). The 3.5 million voxels (left) are input as 32bit keys and placed into buckets of ≤ 512 items, averaging 409 each (center). Each bucket then ..."
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Cited by 15 (4 self)
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Figure 1: Overview of our construction for a voxelized Lucy model, colored by mapping x, y, and z coordinates to red, green, and blue respectively (far left). The 3.5 million voxels (left) are input as 32bit keys and placed into buckets of ≤ 512 items, averaging 409 each (center). Each bucket then builds a cuckoo hash with three subtables and stores them in a larger structure with 5 million entries (right). Closeups follow the progress of a single bucket, showing the keys allocated to it (center; the bucket is linear and wraps around left to right) and each of its completed cuckoo subtables (right). Finding any key requires checking only three possible locations. We demonstrate an efficient dataparallel algorithm for building large hash tables of millions of elements in realtime. We consider two parallel algorithms for the construction: a classical sparse perfect hashing approach, and cuckoo hashing, which packs elements densely by allowing an element to be stored in one of multiple possible locations. Our construction is a hybrid approach that uses both algorithms. We measure the construction time, access time, and memory usage of our implementations and demonstrate realtime performance on large datasets: for 5 million keyvalue pairs, we construct a hash table in 35.7 ms using 1.42 times as much memory as the input data itself, and we can access all the elements in that hash table in 15.3 ms. For comparison, sorting the same data requires 36.6 ms, but accessing all the elements via binary search requires 79.5 ms. Furthermore, we show how our hashing methods can be applied to two graphics applications: 3D surface intersection for moving data and geometric hashing for image matching.
Parallel Hashing Algorithms on BSP and QSM Models
"... We study two parallel computing models – the Bulk Synchronous Parallel (BSP) and the Queued Shared Memory (QSM) – as alternatives to the PRAM model to provide more accurate performance predictions and analyses, and compares the two models in detail. As a case study, we consider a simple hashing pro ..."
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Cited by 1 (0 self)
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We study two parallel computing models – the Bulk Synchronous Parallel (BSP) and the Queued Shared Memory (QSM) – as alternatives to the PRAM model to provide more accurate performance predictions and analyses, and compares the two models in detail. As a case study, we consider a simple hashing problem, design the two versions – the message passing version and the shared memory version – of the algorithm, and compare their run time analytically. The message passing version of the algorithm is implemented and the experiments are performed to display the accuracy and the limitations of the predicted performance analysis. 1.