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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 26 (6 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.
Backyard Cuckoo Hashing: Constant WorstCase Operations with a Succinct Representation
, 2010
"... The performance of a dynamic dictionary is measured mainly by its update time, lookup time, and space consumption. In terms of update time and lookup time there are known constructions that guarantee constanttime operations in the worst case with high probability, and in terms of space consumption ..."
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Cited by 13 (5 self)
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The performance of a dynamic dictionary is measured mainly by its update time, lookup time, and space consumption. In terms of update time and lookup time there are known constructions that guarantee constanttime operations in the worst case with high probability, and in terms of space consumption there are known constructions that use essentially optimal space. In this paper we settle two fundamental open problems: • We construct the first dynamic dictionary that enjoys the best of both worlds: we present a twolevel variant of cuckoo hashing that stores n elements using (1+ϵ)n memory words, and guarantees constanttime operations in the worst case with high probability. Specifically, for any ϵ = Ω((log log n / log n) 1/2) and for any sequence of polynomially many operations, with high probability over the randomness of the initialization phase, all operations are performed in constant time which is independent of ϵ. The construction is based on augmenting cuckoo hashing with a “backyard ” that handles a large fraction of the elements, together with a deamortized perfect hashing scheme for eliminating the dependency on ϵ.
Some Open Questions Related to Cuckoo Hashing
"... Abstract. The purpose of this brief note is to describe recent work in the area of cuckoo hashing, including a clear description of several open problems, with the hope of spurring further research. 1 ..."
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Cited by 12 (4 self)
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Abstract. The purpose of this brief note is to describe recent work in the area of cuckoo hashing, including a clear description of several open problems, with the hope of spurring further research. 1
On the Vulnerability of Hardware Hash Tables to Sophisticated Attacks
"... Abstract. Peacock and Cuckoo hashing schemes are currently the most studied implementations for hardware network systems such as NIDS, Firewalls, etc.. In this work we evaluate their vulnerability to sophisticated complexity Denial of Service (DoS) attacks. We show that an attacker can use insertio ..."
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Abstract. Peacock and Cuckoo hashing schemes are currently the most studied implementations for hardware network systems such as NIDS, Firewalls, etc.. In this work we evaluate their vulnerability to sophisticated complexity Denial of Service (DoS) attacks. We show that an attacker can use insertion of carefully selected keys to hit the Peacock and Cuckoo hashing schemes at their weakest points. For the Peacock Hashing, we show that after the attacker fills up only a fraction (typically 5%−10%) of the buckets, the table completely loses its ability to handle collisions, causing the discard rate of new keys to increase dramaticallybetween 102 and 104 times higher. For the Cuckoo Hashing, we show an attack that can impose on the system an excessive number of memory accesses and degrade its performance. We analyze the vulnerability of the system as a function of the critical parameters and provide simulations results as well.
Balls into Bins made Faster
"... Abstract. Ballsintobins games describe in an abstract setting several multiplechoice scenarios, and allow for a systematic and unified theoretical treatment. In the process that we consider, there are n bins, initially empty, and m = ⌊cn ⌋ balls. Each ball chooses independently and uniformly at r ..."
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Abstract. Ballsintobins games describe in an abstract setting several multiplechoice scenarios, and allow for a systematic and unified theoretical treatment. In the process that we consider, there are n bins, initially empty, and m = ⌊cn ⌋ balls. Each ball chooses independently and uniformly at random k ≥ 3 bins. We are looking for an allocation such that each ball is placed into one of its chosen bins and no bin has load greater than 1. How quickly can we find such an allocation? We present a simple and novel algorithm that finds such an allocation (if it exists) and runs in linear time with high probability. Our algorithm finds applications in finding perfect matchings in a special class of sparse random bipartite graphs, orientation of random hypergraphs, load balancing and hashing. 1
Maximum Bipartite Matching Size And Application to Cuckoo Hashing
"... Cuckoo hashing with a stash is a robust highperformance hashing scheme that can be used in many reallife applications. It complements cuckoo hashing by adding a small stash storing the elements that cannot fit into the main hash table due to collisions. However, the exact required size of the stas ..."
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Cuckoo hashing with a stash is a robust highperformance hashing scheme that can be used in many reallife applications. It complements cuckoo hashing by adding a small stash storing the elements that cannot fit into the main hash table due to collisions. However, the exact required size of the stash and the tradeoff between its size and the memory overprovisioning of the hash table are still unknown. We settle this question by investigating the equivalent maximum matching size of a random bipartite graph, with a constant leftside vertex degree d = 2. Specifically, we provide an exact expression for the expected maximum matching size and show that its actual size is close to its mean, with high probability. This result relies on decomposing the bipartite graph into connected components, and then separately evaluating the distribution of the matching size in each of these components. In particular, we provide an exact expression for any finite bipartite graph size and also deduce asymptotic results as the number of vertices goes to infinity. We also extend our analysis to cases where only part of the leftside vertices have a degree of 2; as well as to the case where the set of rightsize vertices is partitioned into two subsets, and each