8.2 Amdahl's Law and speedup analysis

Amdahl's Law is a game-changer in parallel computing. It shows us how much faster our programs can run when we use multiple processors. But there's a catch – the speedup is limited by the parts of the program that can't be split up.

This law helps us figure out if adding more processors is worth it. Sometimes, it's better to make the non-parallel parts of our code faster instead. It's all about finding the right balance to get the best performance boost.

Amdahl's Law in Parallel Computing

Concept and Implications

Top images from around the web for Concept and Implications

• 

  Amdahl's law - Wikipedia View original

  Is this image relevant?

• 

  Amdahl's Law | Introduction to High Performance and High Throughput Computing View original

  Is this image relevant?

• 

  Amdahl's Law | Introduction to High Performance and High Throughput Computing View original

  Is this image relevant?

• 

  Amdahl's law - Wikipedia View original

  Is this image relevant?

• 

  Amdahl's Law | Introduction to High Performance and High Throughput Computing View original

  Is this image relevant?

1 of 3

Top images from around the web for Concept and Implications

• 

  Amdahl's law - Wikipedia View original

  Is this image relevant?

• 

  Amdahl's Law | Introduction to High Performance and High Throughput Computing View original

  Is this image relevant?

• 

  Amdahl's Law | Introduction to High Performance and High Throughput Computing View original

  Is this image relevant?

• 

  Amdahl's law - Wikipedia View original

  Is this image relevant?

• 

  Amdahl's Law | Introduction to High Performance and High Throughput Computing View original

  Is this image relevant?

1 of 3

• Amdahl's Law is a formula used to predict the theoretical speedup of a program when using multiple processors, taking into account both the parallelizable and serial portions of the program
• The law demonstrates that the speedup of a program using multiple processors is limited by the serial portion of the program, which cannot be parallelized
  • As the number of processors increases, the speedup reaches a limit determined by the serial portion, regardless of how many additional processors are added (diminishing returns)
  • For example, if 10% of a program is serial, the maximum theoretical speedup is limited to 10 times, even with an infinite number of processors
• Amdahl's Law implies that to achieve significant speedup, the parallel portion of the program should be as large as possible, and the serial portion should be minimized
  • Optimizing the serial portion can lead to better overall speedup than adding more processors
  • For instance, reducing the serial portion from 20% to 10% can double the maximum theoretical speedup

Speedup Calculation Formula

• The speedup is calculated as $S(n) = 1 / ((1 - P) + P/n)$, where:
  • $P$ is the fraction of the program that can be parallelized
  • $(1 - P)$ is the fraction that must be executed serially
  • $n$ is the number of processors
• For example, if 75% of a program can be parallelized ($P = 0.75$) and it is executed on 4 processors ($n = 4$), the speedup would be $S(4) = 1 / ((1 - 0.75) + 0.75/4) ≈ 2.67$

Theoretical Speedup Calculation

Steps to Calculate Speedup

• To calculate the theoretical speedup using Amdahl's Law:
  1. Determine the fraction of the program that can be parallelized ($P$) and the fraction that must be executed serially ($1 - P$)
  2. Identify the number of processors ($n$) available for the parallel execution
  3. Substitute the values of $P$ and $n$ into the Amdahl's Law formula: $S(n) = 1 / ((1 - P) + P/n)$
  4. Perform the arithmetic calculations to obtain the theoretical speedup value
• Example: For a program with 60% parallelizable code ($P = 0.6$) running on 8 processors ($n = 8$), the theoretical speedup is $S(8) = 1 / ((1 - 0.6) + 0.6/8) ≈ 2.5$

Impact of Parallelizable Fraction and Processor Count

• The fraction of parallelizable code ($P$) significantly affects the theoretical speedup
  • A higher value of $P$ leads to better speedup potential
  • For example, if $P = 0.9$ (90% parallelizable) and $n = 16$, the speedup is $S(16) ≈ 6.4$, whereas if $P = 0.5$ (50% parallelizable), the speedup is only $S(16) ≈ 1.9$
• Increasing the number of processors ($n$) provides diminishing returns in speedup
  • The speedup curve flattens as $n$ increases, especially when the serial portion is substantial
  • For instance, with $P = 0.8$, the speedup for $n = 4$ is $S(4) ≈ 2.5$, while for $n = 32$, it is $S(32) ≈ 3.5$, showing a smaller improvement despite an 8-fold increase in processors

Amdahl's Law Limitations

Assumptions and Real-World Constraints

• Amdahl's Law assumes that the problem size remains constant as the number of processors increases, which may not always be the case in real-world applications
  • Some problems can be scaled up to utilize more processors effectively (weak scaling)
• The law does not account for the overhead associated with parallelization, such as communication and synchronization between processors, which can reduce the actual speedup
  • The overhead can become significant as the number of processors increases, limiting the scalability
• Amdahl's Law assumes that the workload can be evenly distributed among the processors, which may not be possible for certain problems with dependencies or load imbalances
  • Uneven workload distribution can lead to some processors being idle while others are overloaded, reducing the overall efficiency

Other Factors Affecting Parallel Performance

• The law does not consider the memory hierarchy and data locality, which can significantly impact the performance of parallel programs
  • Accessing shared memory or remote data can introduce latency and bandwidth bottlenecks
  • Proper data distribution and locality optimization are crucial for achieving good parallel performance
• Amdahl's Law focuses on the speedup of a single program, but in practice, parallel systems often run multiple programs concurrently, leading to resource contention and reduced speedup
  • Contention for shared resources such as memory, cache, or interconnect can degrade the performance of individual programs
• The law assumes that the serial portion of the program cannot be further optimized or parallelized, which may not always be true, as some seemingly serial tasks might be parallelizable with advanced techniques
  • Techniques such as pipelining, speculative execution, or algorithmic changes can sometimes reduce the serial portion and improve speedup

Speedup Analysis Techniques

Measuring and Comparing Speedup

• Measure the execution time of the original sequential program ($T_{seq}$) and the parallel program ($T_{par}$) under different processor configurations
• Calculate the actual speedup achieved by the parallel program using the formula: $S_{actual} = T_{seq} / T_{par}$
  • For example, if the sequential program takes 100 seconds and the parallel program with 4 processors takes 30 seconds, the actual speedup is $S_{actual} = 100 / 30 ≈ 3.33$
• Compare the actual speedup with the theoretical speedup predicted by Amdahl's Law to assess the effectiveness of the parallel optimizations
  • If the actual speedup is significantly lower than the theoretical speedup, investigate the reasons for the discrepancy, such as parallelization overhead, load imbalance, or memory bottlenecks

Profiling and Optimization Strategies

• Analyze the scalability of the parallel program by measuring the speedup for different problem sizes and processor counts, to determine if the speedup improves with larger problems or more processors
  • Strong scaling: Fixing the problem size and increasing the number of processors
  • Weak scaling: Increasing both the problem size and the number of processors proportionally
• Use profiling tools to identify the performance bottlenecks and the portions of the program that consume the most time, to focus optimization efforts on the critical sections
  • Tools such as Intel VTune, GNU gprof, or OpenMP profiling interfaces can provide valuable insights into the program's behavior
• Experiment with different parallelization strategies, such as task parallelism, data parallelism, or pipeline parallelism, to find the most suitable approach for the specific problem and architecture
  • Task parallelism: Distributing independent tasks across processors
  • Data parallelism: Partitioning data and performing the same operation on each partition simultaneously
  • Pipeline parallelism: Splitting the computation into stages and processing data through the stages concurrently