Podcast
Questions and Answers
What is the fundamental purpose of High Performance Computing (HPC)?
What is the fundamental purpose of High Performance Computing (HPC)?
Which of the following is NOT a key driver for the need for HPC?
Which of the following is NOT a key driver for the need for HPC?
In which fields is HPC particularly crucial for simulations and modeling?
In which fields is HPC particularly crucial for simulations and modeling?
What limitation does HPC address regarding traditional computing systems?
What limitation does HPC address regarding traditional computing systems?
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What is one significant characteristic of HPC compared to regular desktop computing?
What is one significant characteristic of HPC compared to regular desktop computing?
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Which of the following best describes the SISD architecture?
Which of the following best describes the SISD architecture?
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Which category of parallelism is characterized by processing many data items in the same manner at the same time?
Which category of parallelism is characterized by processing many data items in the same manner at the same time?
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What type of architecture involves parallelism coming from both data and instructions in shared memory?
What type of architecture involves parallelism coming from both data and instructions in shared memory?
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In terms of Flynn's Taxonomy, what type of parallelism does MISD represent?
In terms of Flynn's Taxonomy, what type of parallelism does MISD represent?
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How does pipelining improve processing efficiency?
How does pipelining improve processing efficiency?
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What is one limitation of increasing clock speeds in processors?
What is one limitation of increasing clock speeds in processors?
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Which of the following accurately describes serial computing?
Which of the following accurately describes serial computing?
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In which field is high-performance computing (HPC) applied for drug designing and genome sequencing?
In which field is high-performance computing (HPC) applied for drug designing and genome sequencing?
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What is a key application of HPC in civil engineering?
What is a key application of HPC in civil engineering?
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Which aspect of high-performance computing is crucial for managing high-resolution media content?
Which aspect of high-performance computing is crucial for managing high-resolution media content?
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What does parallel computing offer as a solution for large problems?
What does parallel computing offer as a solution for large problems?
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In what area of engineering is HPC used for aerodynamics simulation?
In what area of engineering is HPC used for aerodynamics simulation?
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Which application area of HPC is involved in understanding geological structures?
Which application area of HPC is involved in understanding geological structures?
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Study Notes
High Performance Computing (HPC)
- HPC is the practice of combining computing power to get much higher performance than a typical desktop or workstation computer. This allows solutions to complex problems in science, engineering, and business
- HPC is driven by the exponential growth of data generation. This includes data from sensors, machines, humans, video, and social media. The complexity of relationships in this data also increases.
- The need for complex simulations and models in fields like climate science, physics, and bioinformatics drives the demand for greater computing power.
- Single-core processors have limitations on needed resources for particular simulations
- Creating processors with faster clock speeds is difficult due to cost and power/heat constraints.
- Adding large amounts of memory to a single processor is expensive.
Background and Contact Information
- Dr. Maha Dessokey is a researcher at the Electronics Research Institute, specifically the HPC and Big Data lab.
- She has worked at the ERI Scientific Cloud Center of Excellence since 2015.
- She has been a researcher at the Electronics Research Institute since 2007.
- Her interests are HPC, Cloud Computing, and Big Data Engineering.
- Her contact email is [email protected]
Resources
- Lectures are available
- The textbook Designing and Building Parallel Programs by Foster, lan is the suggested resource.
- The book is published by Pearson on May 24, 2019.
- Study notes are also provided.
Agenda
- The agenda includes what HPC is, key drivers of HPC, application areas in science & engineering, parallel processing, classification taxonomy (Flynn's), and performance metrics.
What is HPC?
- High Performance Computing is a method of aggregating computing power to achieve higher performance than a typical desktop or workstation. It's used to solve complex problems in science, engineering or business.
Key Drivers of HPC
- Increased Data Generation: Rapid growth in data from various sources (sensors, machines, humans, video, social media).
- Complexity of Relationships: The data relationships are growing more intricate, needing more power to manage, govern and analyze.
- Complex Simulations & Modeling: The need for complex simulations and models is growing in fields such as climate science, physics, and bioinformatics requiring more powerful computing.
- Single-core Processor Limitations: Single-core processors cannot meet resource demands of modern simulations.
- Clock Speed Constraints: Creating processors with faster clock speeds becomes difficult due to cost and power/heat problems.
- Expensive Memory: Increasing memory size on a single processor is costly.
Serial Computing
- A system where only one instruction can be executed at a time.
- Instructions are processed sequentially, one after the other.
Parallel Computing
- A form of computation that executes many calculations simultaneously.
- Large problems are broken down into smaller, parallel problems.
Application Areas of HPC in Science & Engineering
- Space Science: Astrophysics and astronomy applications.
- Earth Science: Studying geological structures, water resources, and seismic exploration.
- Atmospheric Science: Climate and weather forecasting, air quality forecasting.
- Life Science: Applications in drug design, genome sequencing, and protein folding.
- Nuclear Science: Nuclear power, nuclear medicine technology, and defense applications.
- Nano Science: Applications in semiconductor physics, microfabrication, molecular biology and new material exploration.
- Engineering: Crash simulations for automobiles and mechanical engineering, aircraft design and aerodynamic simulations, structural analysis for civil engineering and architecture.
- Multimedia and Animation: Enhancing high resolution, visual effects rendering techniques, real-time rendering, physics modeling, and large data processing.
HPC in Multimedia & Animation
- Increased Complexity: Demand for high resolution (4K, 8K) and advanced visual effects.
- Complex Effects: Heavy computations needed for advanced visual effects and rendering.
- Real-time Rendering: Real-time rendering for gaming and VR applications.
- Simulation: Realistic physics simulations for animations and gaming environments.
- Large Data Processing: Managing and processing massive datasets (e.g., videos, 3D models).
HPC Applications
- Various applications across many diverse fields.
Parallel Processing
- A method of dividing a task into smaller, simultaneous subtasks.
- This is used in many different applications now.
A Quick Review – The von Neumann Architecture Model
- This architecture stores both programs and data in memory.
- Instructions and data are fetched from memory, decoded, and then executed.
Flynn's Classical Taxonomy
- A classification of computer architectures based on the multiplicity of instruction and data streams.
- Categories are defined:
- SISD (Single Instruction, Single Data): Uniprocessor architectures (sequential).
- SIMD (Single Instruction, Multiple Data): Parallelism from data
- MISD (Multiple Instruction, Single Data): Systolic arrays and pipelines
- MIMD (Multiple Instruction, Multiple Data): Shared memory and distributed memory.
Pipelining
- A technique where multiple instructions are overlapped in execution.
- It processes tasks in stages.
- Pipelining takes less time than executing tasks sequentially.
Types of Parallelism
- Data Parallelism: Many data items are processed similarly.
- Functional Parallelism: Programs consist of independent modules that can run in parallel.
- Overlapped/Temporal Parallelism: Tasks within a program are overlapped or executed in parallel using techniques like pipelining.
Performance Issues
- Overhead during parallelization
- Redundancy, interprocessor communication, imbalance in computations
- Efficiency ratios between sequential and parallel parts of an application.
Performance Metrics
- Speedup: Ratio of completion time on one processor to completion time on multiple processors.
- Efficiency: Useful parallel time divided by overall parallel time multiplied by the number of processors
- Throughput: Amount of work done per unit of time.
- MIPS/MFLOPS: Measures of performance based on millions of instructions or floating-point operations per second (useful for specific computations).
- Application Related Measures: Measures based on specific computational tasks (e.g., particle interactions).
Performance Metrics – Example
- The example shows how performance metrics are calculated for a simple equation involving multiple variables being calculated simultaneously.
Summary
- Topics covered: What is HPC?, Why HPC is needed, Applications in Science & Engineering, Flynn's Classical Taxonomy, & Performance Metrics.
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Description
This quiz explores the fundamentals of High Performance Computing (HPC), highlighting its significance in solving complex problems in various fields such as science, engineering, and business. Delve into the challenges and advancements driving the need for enhanced computing capabilities amidst the exponential growth of data generation.
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