N
Naive path tracing (NPT), random walks
406–410
Nanotubes, MD calculations
65–68
n
-body algorithm, Barnes-Hut
algorithm overview
76–77
CUDA implementation
basic problem
75–76
evaluation methodology
88–89
global optimizations
78–79
implementation limitations
91
implementation results
89–90
kernel 1 optimization
79–80
kernel 2 optimization
80–81
kernel 3 optimization
81–83
kernel 4 optimization
83
kernel 5 optimization
84–86
kernel 6 optimization
86
optimizations overview
86–88
n
-body simulations
fast
114–120
M2L kernel implementation
125–130
overview
113–114
performance enhancements
120–122
P2P kernel implementation
122–125
Neuronal spike streams, data mining
core methodology
212–214
definition
211
GPU parallelization
datasets and testbed
222
one thread per occurrence performance
222–224
one thread per occurrence strategy
215–219
overview
214–215
two-pass elimination approach
219–222
two-pass elimination performance
224–226
overview
211–212
serial episode mining
214
Neuroscience, temporal data mining
core methodology
212–214
GPU parallelization
datasets and testbed
222
one thread per occurrence performance
222–224
one thread per occurrence strategy
215–219
overview
214–215
two-pass elimination approach
219–222
two-pass elimination performance
224–226
overview
211–212
serial episode mining
214
Next-generation sequencing (NGS) technology, and GPU computing
153–154
Nonlinear filters, speed-limit-sign recognition
507
Nonrecurring engineering (NRE), LDPC problem
619
Normalization values
CGTOs
9
FFT for speed-limit-sign recognition
507
MaxEnt model
282–286
object-detection pipeline with CUDA
530–532
Normalized root mean square (NRMS), CT reconstruction parameters
698
Normalized Scanpath Saliency (NSS), VSM evaluation
470
Normal updates, facial animation
423–424
NPT
NRE
NRMS
NSGA-II, OS-SIRT
697–698
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