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Lecture 10. Measurement and Timing
Lecture 10. Measurement and Timing
In this video, the speakers discuss various aspects of measurement and timing in computing, including external factors that can affect accuracy. They emphasize the need for models and clear understanding of data, reducing variance to compensate for errors, and using appropriate timing calls to ensure reliability. They also discuss challenges in measuring software performance accurately, such as the power law and non-deterministic factors, while highlighting tools like performance modeling, timing calls, hardware counters, and simulators. Finally, they caution against relying on a single tool, recommending triangulation and adaptation as techniques to ensure accurate results.
The speaker explains the importance of reliable performance measurement in performance software engineering. He recommends the use of statistics to accurately measure performance, and discusses various types of summary statistics such as the mean, which can provide useful measures of performance in different contexts. He points out that taking the arithmetic mean of ratios is not a valid approach, and suggests using the geometric mean instead. The speaker emphasizes the importance of choosing the correct way to aggregate data when comparing programs, and suggests taking the low order statistic, such as the minimum or 10%, from multiple runs to more accurately compare performance. The speaker also discusses different methodologies for measuring and comparing performance, including head-to-head comparison and fitting a model to derive statistics, but warns about the issue of over-fitting in modeling. Overall, the video stresses the importance of reliable performance measurement for improving program performance.
Lecture 11. Storage Allocation
Lecture 11. Storage Allocation
The lecturer discusses storage allocation in this video, covering both memory allocation and freeing. Different types of storage are addressed, including the stack and the heap, as well as fixed and variable size allocation schemes. External fragmentation caused by blocks of memory being spread out is also discussed, with solutions such as free lists or bitmaps per disk page. The concept of garbage collection is also introduced, including reference counting and the limitations of this algorithm. The "mark and sweep" and "stop and copy" garbage collection procedures are also explained, with a focus on how the latter addresses fragmentation and the possible correctness issue created by pointer updates. The video concludes with notes on the time and space complexity of the stop and copy algorithm and its elimination of external fragmentation.
This video covers various topics related to dynamic storage allocation, including the Buddy system, mark and sweep procedures, optimizations, generational and real-time garbage collection, multi-threaded storage allocation, and parallel garbage collection. The lecturer explains that generational garbage collection is based on the idea that younger objects are scanned more frequently, while real-time garbage collection runs in the background during program execution, but can lead to correctness issues if the object and pointer graph keeps changing. Finally, the lecture summarizes different types of storage allocation, including stack and heap, and different methods of garbage collection, such as reference counting, mark-and-sweep, and stop-and-copy. The lecturer mentions that students will get to try some of these methods in their upcoming homework assignment.
Lecture 12. Parallel Storage Allocation
Lecture 12. Parallel Storage Allocation
The video discusses various memory allocation strategies and their trade-offs. The use of malloc and aligned allocation, as well as the importance of proper memory deallocation with free, is explained. The use of Mmap for virtual memory allocation is also covered, along with the issues of external fragmentation and slow performance. The concept of stacks in C and C++ programming is explored, with emphasis placed on a heap-based cactus stack approach for allocating stack frames, which can lead to better space-bound proofs and upper bounds. The use of a pool of linear stacks is also discussed, along with the importance of optimizing for small blocks to minimize fragmentation. The video concludes with a discussion of global and local heap approaches and their potential issues, along with approaches such as bin free lists and periodic memory rebalancing to address these issues.
The video also discusses solutions for parallel storage allocation and introduces the Hoard allocator as a solution. The Hoard allocator uses a combination of local and global heaps and large super blocks that can be moved between heaps to reduce false sharing, improve scalability, and reduce external fragmentation. The maximum storage allocated in the global heap is at most the maximum storage allocated in the local heaps, and the footprint is upper bounded by the user footprint plus allocated but unused storage in the local heaps. The video also briefly discusses other allocators such as Je malloc and Super malloc, with benchmark results indicating that Super malloc performed better than Je malloc and Hoard. The discussion concludes with a reference to online slides for garbage collection details.
Lecture 13. The Cilk Runtime System
Lecture 13. The Cilk Runtime System
The Cilk runtime system is responsible for scheduling and load balancing computations on parallel processors, using a randomized work stealing scheduler to map computations onto processors at runtime. The system is designed to optimize serial execution of the program even at the expense of additional cost to steals. The worker maintains a separate deck data structure that contains pointers to the stack frames and uses head and tail pointers. The frames that are available to be stolen have an additional local structure that contains necessary information for stealing to occur while the deck is external to the call stack. The section explains how the system enables processors to start executing from the middle of a running function and how synchronization between processors becomes complicated when reaching a sync statement that cannot execute beyond the point because specific processors are still waiting for computations to finish. Additionally, the speaker addresses the performance of the system, design considerations and data structures, and how the system optimizes execution using the THC protocol, involving two sets of protocols, one for the worker executing work and one for the thief.
The Cilk Runtime System uses a set jump and long jump protocol to handle the theft of computations from victim processes' execution decks. The Cactus Stack abstraction allows the thief process to have its own call stack to prevent corruption of the victim's stacks. The system's synchronization protocol uses a tree of computations and a Cactus Stack to ensure synchronization only occurs between nested computations within a function. The Full Frame Tree maintains parent-child relationships between computations with outstanding sub-computations to simplify the synchronization process. Additionally, the runtime system optimizes the common case where the current function has no outstanding child computations and all workers are busy. Other features supported include C++ exceptions, reducer hyper objects, and pedigrees.
Lecture 14. Caching and Cache-Efficient Algorithms
Lecture 14. Caching and Cache-Efficient Algorithms
In the video on caching and cache-efficient algorithms, the instructor explains the cache hierarchy of modern machines, the differences between fully associative and direct mapped caches, and the advantages and disadvantages of set associative caches. The video also introduces the ideal cache model and the concept of cache-efficient algorithms. The speaker discusses the cache miss lemma, the tall cache assumption, and the sub-matrix caching lemma. They also analyze cache misses incurred when reading a sub-matrix and during matrix multiplication. The video concludes by introducing the concept of tiled matrix multiplication and how it can significantly improve performance, but also notes that it is not portable and can be expensive to optimize for multiple levels of cache.
The lecture covers caching and cache-efficient algorithms, using recursive matrix multiplication as an example. The speaker emphasizes the importance of analyzing both work and cache misses separately and notes that cache-aware algorithms may not be optimal for all machines due to differing cache sizes. The speaker also discusses cache-oblivious algorithms that auto-tune for any cache size and mentions developments in parallel cache-oblivious code. Finally, the goal is to design algorithms that are either cache-aware or cache-oblivious, and the discussion on cache-oblivious algorithm design will continue in the following lecture.
Lecture 15. Cache-Oblivious Algorithms
Lecture 15. Cache-Oblivious Algorithms
The video discusses cache-oblivious algorithms, which can automatically tune for the cache size on a machine, achieve good cache efficiency and do not require knowledge of the cache parameters of the machine. The video shows how to write code for simulating heat diffusion through differential equations using the stencil method on a 2D matrix. The code has both looping and trapezoidal versions, with the latter being more cache-efficient but not significantly faster in a 2D simulation due to the predictability of the looping code's access pattern. The video also discusses the interplay between caching and parallelism and the diagnosis of potential parallel speed-up bottlenecks. Finally, the speaker explains stencil computations and how each point in an array is updated using a fixed pattern called a stencil, which can suffer from cache misses and has storage requirements that can be reduced using efficient algorithms exploiting temporal and spatial locality.
The second part of the video discusses cache-oblivious algorithms for sorting and merging. Specifically, the video covers the cache complexity of merge sort, introduces the concept of multi-way merging, and explains the cache complexity of the multi-way merge algorithm. The video also discusses the funnel sort algorithm, which is a cache-oblivious sorting algorithm that can merge K squared elements and K sorted lists. The funnel sort algorithm is optimal and recursively constructed with square root of K funnels. The video explains that there are many other cache-oblivious algorithms and data structures, such as b-trees, ordered file maintenance, and priority queues. Overall, the video provides an introduction to cache-oblivious algorithms for those interested in learning more about the topic.
Lecture 16. Nondeterministic Parallel Programming
Lecture 16. Nondeterministic Parallel Programming
This video discusses various concepts related to deterministic and non-deterministic parallel programming. The speaker emphasizes the importance of avoiding non-determinism as it can lead to anomalous behaviors and difficult debugging. Strategies for managing non-determinism include using fixed values, record replay, analysis tools, encapsulation, and unit testing. The use of mutexes is explored in detail, including spinning vs. yielding, reentrant vs. non-reentrant, and fair vs. unfair. The speaker also touches on the concept of context switching and the "ski rental problem" in the context of parallel programming. The segment concludes by discussing basic principles of performance engineering in multi-core processors.
The second part of the video covers the problem of deadlock in parallel programming and offers solutions to avoid it, such as linear ordering of mutexes that guarantees there is no cycle of waiting. Additionally, the speaker introduces the concept of transactional memory, which ensures atomicity by representing a critical region as a transaction that commits all updates at once. The video then presents an algorithm that uses a lock-based approach with a finite ownership array and a release sort require method to prevent deadlocks and starvation without needing a global lock. Lastly, the algorithm release sort and reacquire is explained, which prevents multiple locks from trying to acquire a lock simultaneously, avoiding performance issues.
Lecture 17. Synchronization Without Locks
Lecture 17. Synchronization Without Locks
Charles Leiserson explores the concept of synchronization without locks in a YouTube video. He provides an example which demonstrates the need for a global linear order of instructions to ensure sequential execution, and discusses how mutual exclusion can be achieved through sequential consistency, avoiding the difficulties and potential problems of using locks. Leiserson presents Peterson's algorithm as a solution that uses only load and store operations to access critical sections without interference from concurrent processes. The video also covers the challenges of synchronization through memory due to hardware reordering, and the concept of memory fences to maintain relative ordering with other instructions. Leiserson argues that supporting sequential consistency for parallel machines is beneficial but difficult to achieve for hardware designers.
The second part of the video discusses the use of memory fences and synchronization instructions in preventing errors in parallel programs. The speaker explores different ways of implementing memory fences, implicitly or explicitly, and the importance of careful engineering and coordination between different teams working on different aspects of a processor. Furthermore, the lecturer discusses the use of CAS instructions as part of the lock-free algorithm in C11 language standard to implement mutexes of n-thread deadlock-free mutual exclusion algorithms using only constant space. Charles Leiserson explains the problem of using locks in a multi-threaded system and the solution of using CAS instead, as a thread holding onto the lock for a long time can potentially block other threads waiting to access the same resource. Additionally, the video highlights the potential issue of ABA problem with compare-and-swap and encourages those interested in the topic to learn more about lock-free algorithms.
Lecture 18. Domain Specific Languages and Autotuning
Lecture 18. Domain Specific Languages and Autotuning
In this video, Professor Saman Amarasignhe from the EECS department at MIT discusses the benefits of using domain-specific languages (DSLs) and autotuning in performance engineering. He emphasizes the importance of DSLs, which capture area-specific domains that are difficult to describe in general-purpose languages, allowing programmers to take advantage of domain experts' knowledge for better performance. Singh discusses optimization of graph processes using DSLs, including graph partitioning and the importance of the shape of the graph in computation. He introduces the DSL Halide for image processing, which enables fast code optimization and portability across machines. He discusses the use of Halide in various industries like Google and Adobe. Ultimately, he highlights the significance of experimenting with different approaches in optimizing code while focusing on parallelism, locality, and redundant work.
The video also talks about the challenges of performance engineering and finding the optimal parameters for a program to run efficiently. The speaker suggests that auto-tuning can address this problem by searching the large parameter space automatically to find the optimal values. He notes that exhaustive search can be impractical, and heuristic-based solutions may not be optimal. Autotuning, which defines a space of acceptable values and iterates based on performance results, can speed up the process of finding optimal solutions. The speaker also discusses the application of autotuning in generating schedules for Try, which was able to produce schedules more efficiently and effectively than exhaustive search.
Lecture 19. Leiserchess Codewalk
Lecture 19. Leiserchess Codewalk
In this YouTube video titled "19. Leiserchess Codewalk", Helen Xu explains the rules of Lesierchess, a game played by two teams with the objective of shooting the opposing team's king or having your king shot. The game has two types of moves, basic and swat moves, and a Ko rule that makes a move illegal if it undoes the opponent's most recent move. Xu dives into various aspects of playing the game including the Fisher time control method, Forsythe-Edwards Notation, Cloud autotester and project organization, evaluating and comparing bots using Elo ratings, move generation, static evaluation, and search algorithms such as alpha-beta, principle variation, subtree pruning, and transposition tables. She also discusses the importance of test infrastructure for optimizing the program and the eval.c file, which contains heuristics that evaluate each square on the board based on the type of piece and its color.
The speaker also delves into the laser coverage aspect of the game, explaining the complicated system of generating all possible moves for a position based on the player's color using a while-true statement. They also explain the importance of correctness in code and how to test for it, suggesting the conversion of representation to ensure correctness before optimization for performance. The speaker also discusses the great flexibility provided by the Leiserchess UCI interface, which allows users to edit tables and commands to their liking, and reassures viewers that the dead code will be cleaned up, and any other bugs should be reported to be fixed.