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- Mary Inaba, Naoki Katohy, Hiroshi Imai
- 2009

In this paper we consider the k-clustering problem for a set S of n points p i = (x i) in the d-dimensional space with variance-based errors as clustering criteria, motivated from the color quantization problem of computing a color lookup table for frame buuer display. As the inter-cluster criterion to minimize, the sum of intra-cluster errors over every… (More)

- Yasuo Ishii, Mary Inaba, Kei Hiraki
- ICS
- 2009

A novel data prefetching method -- access map pattern matching (AMPM) -- that uses "memory access map" is proposed. The AMPM prefetching concentrate hardware resources on collecting the access footprint of the frequently accessed area which we called "hot zones". 2-bit state is associated with each cache lines of hot zone. A set of these states is called… (More)

- Mary Inaba, Naoki Katoh, Hiroshi Imai
- Symposium on Computational Geometry
- 1994

In this paper we consider the<italic>k</italic>-clustering problem for a set <italic>S</italic> of <italic>n</italic> points <inline-equation><f><inf>i</inf>=<fen lp="par"><b>x<inf>i</inf></b><rp post="par"> </fen></f> </inline-equation> in the<italic>d</italic>-dimensional space with variance-based errors as clustering criteria, motivated from the color… (More)

In this paper we consider the k-clustering problem for n points in the d-dimensional space, motivated from the problem of computing a color lookup table for frame buer display, and that of compressing two-dimensional image data. Using the technique of computational geometry, this clustering problem is investigated in a unied manner.

- Junichiro Makino, Kei Hiraki, Mary Inaba
- SC
- 2007

We describe the GRAPE-DR (Greatly Reduced Array of Processor Elements with Data Reduction) system, which will consist of 4096 processor chips each with 512 cores operating at the clock frequency of 500 MHz. The peak speed of a processor chip is 512Gflops (single precision) or 256 Gflops (double precision).
The GRAPE-DR chip works as an attached processor… (More)

- Mary Inaba, Hiroshi Imai
- CCCG
- 2000

We present a novel approach to report approximate as well as exact k-closest pairs for sets of high dimensional points, under the L t-metric, t = 1; : : : ; 1. The proposed algorithms are eecient and simple to implement. They all use multiple shifted copies of the data points sorted according to their position along a space lling curve, such as the Peano… (More)

- Yasuo Ishii, Mary Inaba, Kei Hiraki
- ICS
- 2012

Data prefetching, advanced cache replacement policy, and memory access scheduling are incorporated in modern processors. Typically, each technique holds recently accessed locations independently and controls the memory subsystem based on the prediction of future memory access. Unfortunately, these specific optimizations often increase the implementation… (More)

- Yutaka Sugawara, Mary Inaba, Kei Hiraki
- FPL
- 2005

- Yutaka Sugawara, Mary Inaba, Kei Hiraki
- FPL
- 2004

– Despite its simplicity and its linear time, a serial K-means algorithm's time complexity remains expensive when it is applied to a problem of large size of multidimensional vectors. In this paper we show an improvement by a factor of O(K/2), where K is the number of desired clusters, by applying theories of parallel computing to the algorithm. In addition… (More)