4,517 research outputs found
GPGPU for Difficult Black-box Problems
AbstractDifficult black-box problems arise in many scientific and industrial areas. In this paper, efficient use of a hardware accelerator to implement dedicated solvers for such problems is discussed and studied based on an example of Golomb Ruler problem. The actual solution of the problem is shown based on evolutionary and memetic algorithms accelerated on GPGPU. The presented results prove that GPGPU outperforms CPU in some memetic algorithms which can be used as a part of hybrid algorithm of finding near optimal solutions of Golomb Ruler problem. The presented research is a part of building heterogenous parallel algorithm for difficult black-box Golomb Ruler problem
Optimized Surface Code Communication in Superconducting Quantum Computers
Quantum computing (QC) is at the cusp of a revolution. Machines with 100
quantum bits (qubits) are anticipated to be operational by 2020
[googlemachine,gambetta2015building], and several-hundred-qubit machines are
around the corner. Machines of this scale have the capacity to demonstrate
quantum supremacy, the tipping point where QC is faster than the fastest
classical alternative for a particular problem. Because error correction
techniques will be central to QC and will be the most expensive component of
quantum computation, choosing the lowest-overhead error correction scheme is
critical to overall QC success. This paper evaluates two established quantum
error correction codes---planar and double-defect surface codes---using a set
of compilation, scheduling and network simulation tools. In considering
scalable methods for optimizing both codes, we do so in the context of a full
microarchitectural and compiler analysis. Contrary to previous predictions, we
find that the simpler planar codes are sometimes more favorable for
implementation on superconducting quantum computers, especially under
conditions of high communication congestion.Comment: 14 pages, 9 figures, The 50th Annual IEEE/ACM International Symposium
on Microarchitectur
Towards Quantum Belief Propagation for LDPC Decoding in Wireless Networks
We present Quantum Belief Propagation (QBP), a Quantum Annealing (QA) based
decoder design for Low Density Parity Check (LDPC) error control codes, which
have found many useful applications in Wi-Fi, satellite communications, mobile
cellular systems, and data storage systems. QBP reduces the LDPC decoding to a
discrete optimization problem, then embeds that reduced design onto quantum
annealing hardware. QBP's embedding design can support LDPC codes of block
length up to 420 bits on real state-of-the-art QA hardware with 2,048 qubits.
We evaluate performance on real quantum annealer hardware, performing
sensitivity analyses on a variety of parameter settings. Our design achieves a
bit error rate of in 20 s and a 1,500 byte frame error rate of
in 50 s at SNR 9 dB over a Gaussian noise wireless channel.
Further experiments measure performance over real-world wireless channels,
requiring 30 s to achieve a 1,500 byte 99.99 frame delivery rate at
SNR 15-20 dB. QBP achieves a performance improvement over an FPGA based soft
belief propagation LDPC decoder, by reaching a bit error rate of and
a frame error rate of at an SNR 2.5--3.5 dB lower. In terms of
limitations, QBP currently cannot realize practical protocol-sized
( Wi-Fi, WiMax) LDPC codes on current QA processors. Our
further studies in this work present future cost, throughput, and QA hardware
trend considerations
Computational linear algebra over finite fields
We present here algorithms for efficient computation of linear algebra
problems over finite fields
Error-correcting codes and applications to large scale classification systems
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Includes bibliographical references (p. 37-39).In this thesis, we study the performance of distributed output coding (DOC) and error-Correcting output coding (ECOC) as potential methods for expanding the class of tractable machine-learning problems. Using distributed output coding, we were able to scale a neural-network-based algorithm to handle nearly 10,000 output classes. In particular, we built a prototype OCR engine for Devanagari and Korean texts based upon distributed output coding. We found that the resulting classifiers performed better than existing algorithms, while maintaining small size. Error-correction, however, was found to be ineffective at increasing the accuracy of the ensemble. For each language, we also tested the feasibility of automatically finding a good codebook. Unfortunately, the results in this direction were primarily negative.by Jeremy Scott Hurwitz.M.Eng
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