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An adaptive performance modeling tool for GPU architectures
Sara S. Baghsorkhi
, Matthieu Delahaye
,
Sanjay J. Patel
,
William D. Gropp
,
Wen Mei W. Hwu
Electrical and Computer Engineering
Information Trust Institute
Coordinated Science Lab
National Center for Supercomputing Applications (NCSA)
School of Information Sciences
Siebel School of Computing and Data Science
Center for Global Studies
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Keyphrases
GPU Kernel
100%
Modeling Tools
100%
Adaptive Performance
100%
GPU Architecture
100%
Performance Modeling
100%
Microarchitecture
66%
Performance Model
66%
Performance Bottleneck
66%
Analytical Model
33%
Programmer
33%
Data Parallelism
33%
Execution Time
33%
Compute Unified Device Architecture
33%
Full System
33%
System Complexity
33%
Compiler
33%
Memory Access
33%
Control Divergence
33%
Graph-based
33%
Abstract Interpretation
33%
Scratchpad Memory
33%
Performance Information
33%
NVIDIA GPU
33%
Memory Bank Conflicts
33%
Analytical Performance Models
33%
Workflow Graph
33%
Bank Control
33%
Performance Trends
33%
Conflict Flows
33%
Parallel Benchmarks
33%
Kernel Implementation
33%
Computer Science
Adaptive Performance
100%
Performance Model
100%
Graphics Processing Unit
100%
Microarchitecture
25%
Performance Bottleneck
25%
Execution Time
12%
compute unified device architecture
12%
Workflow
12%
Analytical Model
12%
Control Flow
12%
Abstract Interpretation
12%
Scratchpad Memory
12%
System Complexity
12%
Performance Information
12%
Uncoalesced Memory Access
12%