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The title should be evocative of the main point(s) of the activity. It needs to communicate the full context of the activity on its own as it will show up in places like search returns (e.g. Google) where people won't have any contextual clues. So it should convey the idea that this is a teaching activity, what the subject matter is and what the relevant pedagogical focus is. For example: Solar Radiation: Sample Socratic Questions
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The description should be concise and compelling: typically no more than 1-2 very brief paragraphs.
To prepare for this case study, students do background reading on landslides and rock avalanches and read the introductory portion of Hermanns and Strecker's 1999 article on rock avalanches in Argentina. In class, students receive data (assembled from figures in the article) on bedrock geology and physiography, as well as stereonets showing orientations of prominent joint sets, bedding, and foliations in the bedrock. Their task is to answer the question of why gigantic rock avalanches occur is some places but not others in this part of Argentina. The activity gives students practice in interpreting geologic maps, using stereonets, and peer teaching. Each student receives one of four possible data sets and must ultimately explain his/her analysis to others. The activity also connects structural geology to another geoscience discipline.
What concepts and content should students learn from this activity? Are there higher-order thinking skills (e.g. critical thinking, data analysis, synthesis of ideas, model development) that are developed by this activity? Are there other skills (writing, oral presentation, field techniques, equipment operation, etc.) that are developed by the activity.
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This section should describe how the author determines whether or not students (either individually or collectively) are achieving the learning goals outlined for the activity. Other relevant assessment strategies may also be described in this section.
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Choose the relevant terms that help describe your module. Filling out this section will help other educators in knowing more about this module and determining if it will be a good fit for their curriculum (the characteristics will be located underneath the description). You may choose more than one term for each characteristic category if needed.
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Relevant Parallel Computing Concepts
Recommended Teaching Level
Possible Course Use
Introduction to Computer Science
Parallel Computing Systems
Description about TCPP elements, their purpose, instructions.
Classes: Data vs. Control Parallelism
SIMD/Vector (e.g., SSE, Cray)
Pipelines - Data and control hazards
Pipelines - OoO execution
Pipelines - Single vs. Multicycle
Streams (e.g., GPU)
Simultaneous Multi-Threading, Hyper-Threading
Highly Multithreaded (e.g., MTA)
Heterogeneous (e.g., Cell)
Classes: Shared vs. Distributed Memory
SMP - Buses
NUMA/Shared Memory - Directory-based CC-NUMA
NUMA/Shared Memory - CC-NUMA
Message Passing - Bandwidth
Message Passing - Circuit switching
Message Passing - Diameter
Message Passing - Latency
Message Passing - Routing
Message Passing - Packet Switching
Message Passing - Topologies
Impact on Software
Floating Point Representation
Cycles per Instruction (CPI)
Benchmark - Spec Mark
Benchmark - Bandwidth
Parallel Programming Paradigms: By The Target Machine Model
Parallel Programming Paradigms: By The Control Statements
Parallel Programming Notations: Array Languages
Microprocessor Vector Extensions
Fortran 90/C++ Array Extensions
Parallel Programming Notations: Shared Memory Notations
Parallel Programming Notations: SPMD Notations
Parallel Programming Notations: Functional/Logical Languages
Semantics and Corrections Issues
Tasks and Threads
Tools to Detect Concurrency Defects
Parallel and Distributed Models and Completxity: Costs of Computation
Parallel and Distributed Models and Completxity: Cost Reduction
Space Compression, etc.
Parallel and Distributed Models and Completxity: Cost Tradeoffs
Time vs. Space
Power vs. Time
Parallel and Distributed Models and Completxity: Scalability in Algorithms and Architectures
Scalability in Algorithms and Architectures
Parallel and Distributed Models and Completxity: Notations from Complexity Theory
Parallel and Distributed Models and Completxity: Notations From Scheduling
Divide & Conquer
Scan (Parallel Prefix)
Graph Embedding as an Algorithmic Tool
Graph Algorithms: Sort
Graph Algorithms: Path Selection
Graph Algorithms: Other
Leader Election/Symmetry Breaking
Why and What is Parallel/Distributed Computing?
Security in Distributed System
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