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Jizhu Lu

from Redmond, WA
Age ~62
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Also known as:
Lu Li Jizhu, Jizhu Li
Phone and address:
2912 173Rd Ct NE, Redmond, WA 98052
(302) 286-0156
Related to:
Li ShiyiYali Liang, 48Jizhong S Li, 67Xiao M Liu, 67Xuan Li, 40Lihang Lu, 34Li WeijingYingsi Li, 46Richard Y Li, 56Weijing Li, 63
Connected to:
Abigail I Lu, 32Aiping Lu, 54Allen C Lu, 46Anjian Lu, 84Anna G Lu, 67Anne B Lu, 72Arnold W Lu, 49Ava Lu, 36Ay K Lu, 54Barbara T Lu, 74

Jizhu Lu Phones & Addresses

  • 2912 173Rd Ct NE, Redmond, WA 98052 (302) 286-0156
  • 37 Oklahoma State Dr, Newark, DE 19713
  • 638 Lehigh Rd, Newark, DE 19711
  • 658 Lehigh Rd, Newark, DE 19711
  • Bellevue, WA
  • Athens, GA
  • 658 Lehigh Rd APT G8, Newark, DE 19711

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Jizhu Lu Photo 1

Jizhu Lu

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Location:
Redmond, WA

Publications

Us Patents

Direct Memory Access Transfer Efficiency

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US Patent:
20100161896, Jun 24, 2010
Filed:
Dec 18, 2008
Appl. No.:
12/337703
Inventors:
Jizhu Lu - Newark DE, US
Michael P. Perrone - Yorktown Heights NY, US
Assignee:
International Business Machines Corporation - Armonk NY
International Classification:
G06F 12/08
US Classification:
711112, 711E12019
Abstract:
A mechanism is provided for improving the efficiency of multiple smaller direct memory access transfers. The mechanism uses one input buffer and a small result buffer, or some temporary variables, to temporarily store computation results. The mechanism performs a computation on a segment of data in the input buffer and stores the result in the temporary result buffer. The mechanism then copies the result back into the input buffer. As such, the mechanism uses the input buffer as both an input buffer and a results buffer. The mechanism then performs a direct memory access transfer on the segment of the input buffer that contains the computation result and then performs a computation on the next segment of the input buffer. The mechanism then repeats this process until the entire input buffer has been processed.

Sparse Matrix Padding

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US Patent:
20100306300, Dec 2, 2010
Filed:
May 29, 2009
Appl. No.:
12/474882
Inventors:
Jizhu Lu - Bellevue WA, US
Laurent Visconti - Bainbridge Island WA, US
Assignee:
Microsoft Corporation - Redmond WA
International Classification:
G06F 7/32
US Classification:
708520
Abstract:
Zero elements are added to respective lines (e.g., rows/columns) of a sparse matrix. The added zero elements increase the number of elements in the respective lines to be a multiple of a predetermined even number “n” (e.g., 2, 4, 8, etc.), based upon an n-fold unrolling loop, where n=2, 4, 8, etc. By forming a sparse matrix having lines (e.g., rows or columns) that are multiples of the predetermined number “n”, the n-fold unrolling loop thereby acts upon a predetermined number of elements in respective iterations, avoiding unnecessarily costly operations (e.g., additional loop unrolling code) on remainder non-zero elements (e.g. remainder row/column elements not within an n-fold unrolling loop) left in a row or column after unrolling. This improves the efficiency of sparse matrix linear algebra solvers and key sparse linear algebra kernels (e.g., SPMV) thereby improving the overall performance of a computer (e.g., running an application).

Elimination Of Rounding Error Accumulation

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US Patent:
20210405966, Dec 30, 2021
Filed:
Sep 13, 2021
Appl. No.:
17/473319
Inventors:
JIZHU LU - REDMOND WA, US
LIHANG LU - REDMOND WA, US
Assignee:
CLOUD & STREAM GEARS LLC - REDMOND WA
International Classification:
G06F 7/499
Abstract:
The present invention extends to methods, systems, and computing system program products for elimination of rounding error accumulation in iterative calculations for Big Data or streamed data. Embodiments of the invention include iteratively calculating a function for a primary computation window of a pre-defined size while incrementally calculating the function for one or more backup computation windows started at different time points and whenever one of the backup computation windows reaches a size of the pre-defined size, swapping the primary computation window and the backup computation window. The result(s) of the function is/are generated by either the iterative calculation performed for the primary computation window or the incremental calculation performed for a backup computation window which reaches the pre-defined size. Elimination of rounding error accumulation enables a computing system to steadily and smoothly run iterative calculations for unlimited number of iterations without rounding error accumulation.

Decremental Autocorrelation Calculation For Big Data Using Components

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US Patent:
20180270158, Sep 20, 2018
Filed:
May 22, 2018
Appl. No.:
15/986764
Inventors:
Jizhu Lu - REDMOND WA, US
Assignee:
CLOUD & STREAM GEARS LLC - REDMOND WA
International Classification:
H04L 12/813
H04L 29/08
Abstract:
The present invention extends to methods, systems, and computing system program products for decrementally calculating autocorrelation for Big Data. Embodiments of the invention include decrementally calculating one or more components of autocorrelation at a specified lag for an adjusted computation window based on the one or more components of an autocorrelation at the specified lag calculated for a previous computation window and then calculating the autocorrelation at the specified lag based on one or more of the decrementally calculated components. Decrementally calculating autocorrelation avoids visiting all data elements in the adjusted computation window and performing redundant computations thereby increasing calculation efficiency, saving computing resources and reducing computing system's power consumption.

Iteratively Calculating Standard Deviation For Streamed Data

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US Patent:
20150278159, Oct 1, 2015
Filed:
May 26, 2015
Appl. No.:
14/720984
Inventors:
- Redmond WA, US
Jizhu Lu - Redmond WA, US
International Classification:
G06F 17/18
H04L 29/06
Abstract:
The present invention extends to methods, systems, and computer program products for iteratively calculating standard deviation for streamed data. Embodiments of the invention include iteratively calculating standard deviation in a current computation window based on the standard deviation calculation for a previous computation window. Iteratively calculating standard deviation avoids visiting all previous input and performing redundant computations thereby increasing calculation efficiency. In general, streaming data is added to a buffer of size n until the buffer is filled up. Once the buffer is filled, a sum and standard deviation are calculated for the first n data points. As new data elements are received, a new sum is calculated by reusing the prior sum and a new standard deviation is calculated by reusing the prior standard deviation.

Iteratively Calculating Standard Deviation For Streamed Data

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US Patent:
20140164456, Jun 12, 2014
Filed:
Dec 12, 2012
Appl. No.:
13/711624
Inventors:
- Redmond WA, US
Jizhu Lu - Redmond WA, US
Assignee:
MICROSOFT CORPORATION - Redmond WA
International Classification:
G06F 17/00
US Classification:
708200
Abstract:
The present invention extends to methods, systems, and computer program products for iteratively calculating standard deviation for streamed data. Embodiments of the invention include iteratively calculating standard deviation in a current computation window based on the standard deviation calculation for a previous computation window. Iteratively calculating standard deviation avoids visiting all previous input and performing redundant computations thereby increasing calculation efficiency. In general, streaming data is added to a buffer of size n until the buffer is filled up. Once the buffer is filled, a sum and standard deviation are calculated for the first n data points. As new data elements are received, a new sum is calculated by reusing the prior sum and a new standard deviation is calculated by reusing the prior standard deviation.
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Jizhu Lu from Redmond, WA, age ~62 Get Report