Feature Story Archives

This Week's Multicore Reading List
MATLAB and Google App Engine

Logging In C++ : Part 2
Improving log granularity

A Conversation with BitMagic's Developer
Anatoliy Kuznetsov developed the BitMagic C++ Library to implement efficient platform independent bitsets

Prefer Structured Lifetimes: Local, Nested, Bounded, Deterministic
What's good for the function and the object is also good for the thread, the task, and the lock

Multicore-enabling the N-Queens Problem Using Cilk++

Multicore Storage Allocation
The four basic problems that a good parallel storage allocator solves

QuickPath Interconnect: Rules of the Revolution

This Week's Multicore Reading List
Architectural optimization and C#

A Cute Technique for Avoiding Certain Race Conditions

Scaling Ambient Animations
Putting threaded animation to work

Performance Analysis Tools for Linux Developers: Part 1
Performance analysis and profiling for Intel Processor Architectures

Parallelism and the Cloud
Cloud computing and parallelism have become a fundamental enabling technology

This Week's Multicore Reading List
Patterns and Pragmatics

Avoid Exposing Concurrency: Hide It Inside Synchronous Methods
You have a mass of existing code and want to add concurrency. Where do you start?

Using Erlang to Build Reliable, Fault Tolerant, Scalable Systems
A case study in rebuilding Yahoo! Harvester

Installing Intel Parallel Advisor Lite on Windows 7
There's a simple -- but not necessarily obvious -- way to install Intel Parallel Advisor Lite on Windows 7

Actor Virtual Machine
Extending the OO model with Asynchronous Message Passing and Dynamic Message Binding

Optimization Techniques for Intel Multicore Processors
The key is understanding the inherent parallelism in applications

Algorithm Improvement through Performance Measurement: Part 2
In-place Hybrid Binary-Radix Sort

Multithreaded File I/O
Tackling the file I/O bottleneck

How Theory of Constraints Can Help in Software Optimization
Software optimization is the process of improving the software by eliminating bottlenecks

This Week's Multicore Reading List
Snow Leopard and a new approach to parallel programming

Load Balancing Between Threads
Unequal workloads can eliminate the performance benefit of parallel code

The Three Stages of Preparation for Optimizing Parallel Software
Improving software performance on parallel software requires a structured approach

Visual Studio 2008 SP1 .NET Framework Source Debugging
Debugging the .NET Framework

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Real World Parallelism Webinar Series
  • November 17, 2009
    Visual Effects for Animation - presented by DreamWorks Animation
    Speaker: Ron Henderson (Bio)

    Ron Henderson manages the FX Tools group at DreamWorks Animation, where he is responsible for developing physical simulation and procedural modeling tools. These systems have been used for key visual effects in recent films such as Kung Fu Panda and Monsters vs. Aliens (March 2009).

    Prior to joining DreamWorks in 2002 he was a senior scientist at Caltech with a joint appointment to the Applied Math and Aeronautics departments, where he worked on efficient techniques for the direct numerical simulation of fluid turbulence.

    Abstract:
    In this webinar, Ron Henderson will show examples of visual effects, from hair and feathers to smoke and fire, from a variety of DreamWorks Animation feature films. He will discuss in general terms the kinds of techniques used to achieve particular visual effects. Finally, Henderson will show a detailed breakdown of the dam-breaking scene from Madagascar: Escape 2 Africa, demonstrating how different elements of key frame animation, simulation, and rendering are combined in a real production shot.

  • December 1, 2009
    A Quick and Easy Way to Parallelize a Legacy Codebase with Intel® Threading Building Blocks (TBBs)
    Speaker: Bernard Laberge, Avid, Senior Principal Engineer (Bio)

    Bernard Laberge is a senior principal engineer in the video editors division at Avid. During his seven years with the company he has been actively involved in the replacement of the legacy video processing engines used by Avid editors with a common hardware-abstracted, component-based video processing engine currently running on the CPU with SIMD optimized code, GPU, and dedicated hardware.

    Abstract:
    Learn how to overcome the limitations of a thread-based scheduler, including dealing with the absence of recursive parallelism support and the inefficient handling of unbalanced processing load. Bernard Laberge addresses how Avid resolved the expensive refactoring of their thread-based scheduler into a task-based solution by choosing Intel® Threading Building Blocks (TBBs). He explores how Avid was able to easily integrate the Intel TBBs into their video editor applications and more than 5 million lines of code.

  • December 15, 2009
    How to Use Intel® Parallel Studio to Streamline Code Development in a Multicore Environment
    Speaker: Matt Dunbar, Director for Performance Technology, SIMULIA (Bio)

    Matt Dunbar is the director for performance technology at SIMULIA. Since joining the company in 1993, he has worked on parallelization of the Abaqus suite of products, initially for shared memory architectures and more recently for distributed memory architectures. Dunbar has also been intimately involved in selecting both the hardware and software tools used in the development of the Abaqus product line.

    Abstract:
    Resolve elusive, costly multithreading errors quickly and efficiently with Intel® Parallel Studio. While many coding problems that lead to bugs in software applications are typically straightforward logic errors, errors in managing memory and in multithreading code can sometimes take weeks to months to diagnose and fix. Matt Dunbar explores how and why taking advantage of multicore processors through multithreaded code is critical for compute-intensive applications. While spotlighting his work on SIMULIA's Abaqus finite element solver, Dunbar addresses the need for multicore execution and shares his experiences using Intel Parallel Studio to streamline code development in a multicore environment.