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Lecture 32 : Optimizing Reduction Kernels (Contd.) 37:02
Lecture 32 : Optimizing Reduction Kernels (Contd.)
📺 NPTEL IIT Kharagpur • 👁️ 1,711 views
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Lecture 31 : Optimizing Reduction Kernels (Contd.) 33:07
Lecture 31 : Optimizing Reduction Kernels (Contd.)
📺 NPTEL IIT Kharagpur • 👁️ 1,886 views
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Lecture 33 : Optimizing Reduction Kernels (Contd.) 26:48
Lecture 33 : Optimizing Reduction Kernels (Contd.)
📺 NPTEL IIT Kharagpur • 👁️ 1,433 views
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Lecture 29 : Optimizing Reduction Kernels (Contd.) 40:40
Lecture 29 : Optimizing Reduction Kernels (Contd.)
📺 NPTEL IIT Kharagpur • 👁️ 2,553 views
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Optimized Reduction Kernel Explained | CUDA Warp and Block Reduction 8:15
Optimized Reduction Kernel Explained | CUDA Warp and Block Reduction
📺 Parallel Routines • 👁️ 678 views
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Lecture 34 : Optimizing Reduction Kernels (Contd.) 27:05
Lecture 34 : Optimizing Reduction Kernels (Contd.)
📺 NPTEL IIT Kharagpur • 👁️ 1,427 views
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Lecture 30 : Optimizing Reduction Kernels (Contd.) 27:35
Lecture 30 : Optimizing Reduction Kernels (Contd.)
📺 NPTEL IIT Kharagpur • 👁️ 2,027 views
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Lecture 28 : Optimizing Reduction Kernels 25:07
Lecture 28 : Optimizing Reduction Kernels
📺 NPTEL IIT Kharagpur • 👁️ 3,670 views
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Quantum Machine Learning - 32 - Quantum-Enhanced Kernel Methods 1 (Maria Schuld) 15:53
Quantum Machine Learning - 32 - Quantum-Enhanced Kernel Methods 1 (Maria Schuld)
📺 Quantum ML • 👁️ 11,184 views
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18. GPU Kernel Programming [HPC in Julia] 1:02:33
18. GPU Kernel Programming [HPC in Julia]
📺 Jamie Mair • 👁️ 932 views
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Lecture 27: Memory Access Coalescing (Contd.) 46:11
Lecture 27: Memory Access Coalescing (Contd.)
📺 NPTEL IIT Kharagpur • 👁️ 2,359 views
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CUDA Part F: Kernel Optimizations: Shared Memory Accesses; Peter Messmer (NVIDIA) 21:56
CUDA Part F: Kernel Optimizations: Shared Memory Accesses; Peter Messmer (NVIDIA)
📺 cscsch • 👁️ 10,026 views
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CUDA Programming: Parallel Reduction (GPU Reduce in CUDA) 16:46
CUDA Programming: Parallel Reduction (GPU Reduce in CUDA)
📺 achal • 👁️ 536 views
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Lecture 10 - Reduction 1:21:26
Lecture 10 - Reduction
📺 Programming Massively Parallel Processors • 👁️ 1,557 views
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Lecture 23: Memory Access Coalescing (Contd.) 25:26
Lecture 23: Memory Access Coalescing (Contd.)
📺 NPTEL IIT Kharagpur • 👁️ 4,084 views
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Lecture 32 Optimizing Reduction Kernels Contd Information Guide

  1. Overview to Lecture 32 Optimizing Reduction Kernels Contd
  2. Main Features
  3. History
  4. Expert Insights
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Overview to Lecture 32 Optimizing Reduction Kernels Contd

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History

Lecture 33 : Optimizing Reduction Kernels (Contd.) System Hub
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Optimized Reduction Kernel Explained | CUDA Warp and Block Reduction
Optimized Reduction Kernel Explained | CUDA Warp and Block Reduction
Lecture 34 : Optimizing Reduction Kernels (Contd.)
Lecture 34 : Optimizing Reduction Kernels (Contd.)
Lecture 30 : Optimizing Reduction Kernels (Contd.)
Lecture 30 : Optimizing Reduction Kernels (Contd.)
Lecture 28 : Optimizing Reduction Kernels
Lecture 28 : Optimizing Reduction Kernels
Quantum Machine Learning - 32 - Quantum-Enhanced Kernel Methods 1 (Maria Schuld)
Quantum Machine Learning - 32 - Quantum-Enhanced Kernel Methods 1 (Maria Schuld)
18. GPU Kernel Programming [HPC in Julia]
18. GPU Kernel Programming [HPC in Julia]
Lecture 27: Memory Access Coalescing (Contd.)
Lecture 27: Memory Access Coalescing (Contd.)
CUDA Part F: Kernel Optimizations: Shared Memory Accesses; Peter Messmer (NVIDIA)
CUDA Part F: Kernel Optimizations: Shared Memory Accesses; Peter Messmer (NVIDIA)
CUDA Programming: Parallel Reduction (GPU Reduce in CUDA)
CUDA Programming: Parallel Reduction (GPU Reduce in CUDA)
Lecture 10 - Reduction
Lecture 10 - Reduction
Lecture 23: Memory Access Coalescing (Contd.)
Lecture 23: Memory Access Coalescing (Contd.)

Expert Insights

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Last Updated: August 16, 2026

Future Outlook

Lecture 29 : Optimizing Reduction Kernels (Contd.) System Hub
For 2026, Lecture 32 Optimizing Reduction Kernels Contd remains one of the most talked-about creator profiles. Check back for the newest reports.

Disclaimer: Disclaimer: All Verified Registry logs and creator system metrics are compiled from publicly accessible data, development records, and digital index testing.

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