JetBrains Australia 2019 - Exploring .NET’s memory management – a trip down memory lane

These days fast code needs to operate in harmony with its environment. At the deepest level this means working well with hardware: RAM, disks and SSDs. A unifying theme is treating memory access patterns in a uniform and predictable way that is sympathetic to the underlying hardware. For example writing to and reading from RAM and Hard Disks can be significantly sped up by operating sequentially on the device, rather than randomly accessing the data. In this talk we’ll cover why access patterns are important, what kind of speed gain you can get and how you can write simple high level code which works well with these kind of patterns.

This document provides an overview of big data analytics with Scala, including common frameworks and techniques. It discusses Lambda architecture, MapReduce, word counting examples, Scalding for batch and streaming jobs, Apache Storm, Trident, SummingBird for unified batch and streaming, and Apache Spark for fast cluster computing with resilient distributed datasets. It also covers clustering with Mahout, streaming word counting, and analytics platforms that combine batch and stream processing.

The document summarizes the D programming language compiler, including its organization with one front end and three back ends, major changes including converting the front end to D and using Dwarf exception handling. It describes the source code organization, types of compiles, memory allocation, strings, arrays, parsing, semantic analysis, lowering, constant folding, templates, inlining, and challenges in improving encapsulation, reducing complexity and memory usage.

This document provides an overview of object-oriented concepts in JavaScript. It defines JavaScript and its components like ECMAScript, DOM, and BOM. It discusses how JavaScript supports object-oriented programming features like polymorphism and inheritance through prototypal inheritance despite not being a classical OOP language. The document also covers JavaScript data types, objects, scope, closures, context, and methods for changing contexts like call and apply.

The document discusses garbage collection techniques used in Android, including mark-and-sweep, generational, and copying collection. It covers the goals of memory management in Android including minimizing app launch time and disk space usage. Key garbage collection algorithms described are mark-and-sweep, Cheney's copying algorithm, and concurrent mark-sweep.

This document discusses property-based testing using generators. It introduces the concept of generators and how they can be used to automatically generate random test inputs. The document outlines several key points: - Generators can be composed together using operations like map, zip, and concat to produce complex generators from simple ones. - Generators form algebraic structures like functors, monoids, and monads that allow them to be composed while satisfying certain mathematical laws. - The generator library implements generators as classes with methods derived from these algebraic structures, allowing generators to be easily composed and transformed in a consistent way.

These days fast code needs to operate in harmony with its environment. At the deepest level this means working well with hardware: RAM, disks and SSDs. A unifying theme is treating memory access patterns in a uniform and predictable that is sympathetic to the underlying hardware. For example writing to and reading from RAM and Hard Disks can be significantly sped up by operating sequentially on the device, rather than randomly accessing the data. In this talk we’ll cover why access patterns are important, what kind of speed gain you can get and how you can write simple high level code which works well with these kind of patterns.

This document provides an overview of Span<T> in .NET. It begins with background on memory management in .NET and value vs. reference types. It then defines Span<T> as a new value type that represents contiguous regions of memory, whether managed, unmanaged, or on the stack. It explains that Span<T> allows high-performance, low-overhead access to memory without unsafe code. Examples are given of using Span<T> to represent memory from arrays, strings, and other sources. Benchmarks show Span<T> outperforming alternatives like Substring. Real-world uses like parsing delimited strings are demonstrated.

This document discusses Scala and big data technologies. It provides an overview of Scala libraries for working with Hadoop and MapReduce, including Scalding which provides a Scala DSL for Cascading. It also covers Spark, a cluster computing framework that operates on distributed datasets in memory for faster performance. Additional Scala projects for data analysis using functional programming approaches on Hadoop are also mentioned.

I show how powerful incremental development with Lisp/Scheme is by showing a website and game I created by running the application and developing them real-time.

The document discusses key concepts related to memory management in the .NET CLR, including the heap and stack, value and reference types, pointers, and how objects are allocated in memory. It explains the garbage collection process, including different flavors, generations of objects, and pinning. Large object heap and finalization are also covered as it relates to unmanaged resources. Overall, the document provides a comprehensive overview of memory management in the .NET CLR.

This is second edition of my keynote "On Being a Professional Software Developer" with slide comments (in Russian) which contain main ideas of the keynote. I hope the slides could be used as a standalone reading material.

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This document provides an overview of garbage collection, including: - The first language to use garbage collection was LISP in 1959. - Garbage collection automatically manages memory allocation and deallocation. - Key garbage collection methods are reference counting and tracing. - Implementations include mark-and-sweep and copying collection. - Modern garbage collectors use generational collection to more efficiently handle short-lived and long-lived objects.

Scalding is a Scala library built on top of Cascading that simplifies the process of defining MapReduce programs. It uses a functional programming approach where data flows are represented as chained transformations on TypedPipes, similar to operations on Scala iterators. This avoids some limitations of the traditional Hadoop MapReduce model by allowing for more flexible multi-step jobs and features like joins. The Scalding TypeSafe API also provides compile-time type safety compared to Cascading's runtime type checking.

1) Comparable and Comparator interfaces in Java can both be used to sort collections but have some key differences. Comparable provides a single sort order while Comparator allows multiple sort orders. 2) WritableComparable is a subinterface that extends both Writable and Comparable interfaces. Any type used as a key in Hadoop MapReduce must implement this interface. 3) RawComparator is an optimization Hadoop provides to directly compare byte arrays containing serialized objects instead of deserializing for comparison. WritableComparator implements RawComparator for WritableComparable types.

This talk answers an age-old question: is garbage collection faster/slower/the same speed as malloc/free? We introduce oracular memory management, an approach that lets us measure unaltered Java programs as if they used malloc and free. The result: a good GC can match the performance of a good allocator, but it takes 5X more space. If physical memory is tight, however, conventional garbage collectors suffer an order-of-magnitude performance penalty.

The .NET Garbage Collector (GC) is really cool. It helps providing our applications with virtually unlimited memory, so we can focus on writing code instead of manually freeing up memory. But how does .NET manage that memory? What are hidden allocations? Are strings evil? It still matters to understand when and where memory is allocated. In this talk, we’ll go over the base concepts of .NET memory management and explore how .NET helps us and how we can help .NET – making our apps better. Expect profiling, Intermediate Language (IL), ClrMD and more!

The .NET Garbage Collector (GC) is really cool. It helps providing our applications with virtually unlimited memory, so we can focus on writing code instead of manually freeing up memory. But how does .NET manage that memory? What are hidden allocations? Are strings evil? It still matters to understand when and where memory is allocated. In this talk, we’ll go over the base concepts of .NET memory management and explore how .NET helps us and how we can help .NET – making our apps better. Expect profiling, Intermediate Language (IL), ClrMD and more!

The .NET Garbage Collector (GC) is really cool. It helps providing our applications with virtually unlimited memory, so we can focus on writing code instead of manually freeing up memory. But how does .NET manage that memory? What are hidden allocations? Are strings evil? It still matters to understand when and where memory is allocated. In this talk, we’ll go over the base concepts of .NET memory management and explore how .NET helps us and how we can help .NET – making our apps better. Expect profiling, Intermediate Language (IL), ClrMD and more!

The .NET Garbage Collector (GC) is really cool. It helps providing our applications with virtually unlimited memory, so we can focus on writing code instead of manually freeing up memory. But how does .NET manage that memory? What are hidden allocations? Are strings evil? It still matters to understand when and where memory is allocated. In this talk, we’ll go over the base concepts of .NET memory management and explore how .NET helps us and how we can help .NET – making our apps better. Expect profiling, Intermediate Language (IL), ClrMD and more!

The .NET Garbage Collector (GC) is really cool. It helps providing our applications with virtually unlimited memory, so we can focus on writing code instead of manually freeing up memory. But how does .NET manage that memory? What are hidden allocations? Are strings evil? It still matters to understand when and where memory is allocated. In this talk, we’ll go over the base concepts of .NET memory management and explore how .NET helps us and how we can help .NET – making our apps better. Expect profiling, Intermediate Language (IL), ClrMD and more!

This document discusses .NET systems programming and garbage collection. It covers garbage collection generations, modes, and considerations for minimizing allocations. It also discusses eliminating delegates to reduce allocations, using value types appropriately, avoiding empty collections, and optimizing for thread locality to reduce context switching overhead. Data structures and synchronization techniques are discussed, emphasizing the importance of choosing lock-free data structures when possible to improve performance.

This document provides an overview and agenda for the JPoint 2015 conference. It includes summaries of sessions on memory leaks profiling basics, notes about the Java String class, defining and measuring technical debt, how regular expressions work under the hood, and using memory dumps and analysis tools to find memory leaks. The agenda outlines sessions on memory regions, garbage collection, identifying memory leaks through examples, JVM options for logs and dumps, String class internals, technical debt concepts, and regular expression matching algorithms.

The document discusses memory management and garbage collection in the Hotspot Java Virtual Machine. It describes different garbage collection algorithms like mark-sweep, copying, and generational collection. It explains the different garbage collectors in Hotspot JVM like serial, parallel, parallel compacting, and concurrent mark sweep collectors. It also discusses some key garbage collection terminology and metrics.

The document outlines an advanced workshop on the EnScript programming language, covering topics such as language core concepts, syntax, object model, memory management, APIs, debugging, dialogs, handling evidence, external automation, conditions, reflection, and multithreading. The workshop agenda includes language fundamentals, basic APIs, debugging the execution environment, creating dialogs, handling evidence, external communication, conditions and reflection, and multithreading. Stewart also presents three fundamental laws of EnScript regarding data structures, memory management of NodeClass objects, and the relationship between EnScript classes and EnCase views.

The document discusses optimizing code and data for CPU caches through various techniques like improving data locality, reducing unnecessary memory accesses, and reusing cached data. It covers optimizing code layout, data structures, prefetching, and addressing issues like aliasing.

The document discusses various techniques for optimizing memory usage and cache performance in code. It begins by justifying the need for memory optimization given trends in CPU and memory speeds. It then provides an overview of memory hierarchies and caches. The rest of the document discusses specific techniques for optimizing data structures, prefetching, layout of data in memory, reducing aliasing, and other strategies to improve cache utilization and performance.

- C is a commonly used language for embedded systems due to its portability, efficiency, and conciseness. It was developed in the late 1960s and early 1970s alongside Unix. - C was designed for systems programming tasks like operating systems and compilers. It was influenced by its predecessors BCPL and B and is well-suited for direct hardware manipulation. - C uses expressions, conditional and iterative statements, functions, and other constructs to provide a structured and portable way to write low-level systems code while avoiding the complexity of assembly.

Kafka Streams: Revisiting the decisions of the past (How I could have made it better), Jason Bell, Kafka DevOps Engineer @ Digitalis.io https://www.meetup.com/Cleveland-Kafka/events/272339276/