Top White Papers

A fast, scalable solution for solving the transportation problem

While the family of transportation problems can be solved by hand, at least for relatively small problems, the IMSL Library includes an algorithm that is fast and scalable.

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7 questions to select, deploy, and maintain open source software effectively

A question that often comes up in enterprise software organizations is: What happens when open source software doesn’t work?

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Prioritize defects faster with Klocwork SmartRank

The faster a bug is identified in code, the easier it is to fix. Klocwork SmartRank helps identify which issues to fix first.

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Enterprise API management defined

Defining API management and the best path to quickly capitalize on new revenue channels without assuming risk along the way.

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White Papers

SOA development governance

Software development in the enterprise has never been a simple process. However, in the last decade, with the emergence of SOA, PaaS, private clouds, and cloud hybrids, the SDLC has become exponentially more challenging. A strong development governance platform is essential in this environment. This paper examines how development governance with Akana Repository Manager can solve many of the challenges inherent in the new SDLC.

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SOA implementation strategy

Implementing SOA as a technology solution can increase complexity with many loosely-coupled services that fail to deliver the benefits of SOA. SOA can only be successful if it is well-defined and well-managed. The basis for this is to define the objectives that you expect from your SOA initiative. This paper shares a pragmatic approach to developing such an SOA implementation strategy that aligns the SOA with business.

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Lessons from 29 DevOps experts on the best way to make the transition to continuous delivery

Innovation has changed. Gone are the days when a solitary genius holed up in a garage conceived a big idea, and then painstakingly perfected and brought it to market years later. Today, innovation is fluid, fast moving, and collaborative. Innovation is the engine for growth and value creation in the modern world, and software is the fuel.

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The business case for earlier software defect detection and compliance

Regardless of the industry your business operates in, software is likely all around it. Software powers our cars, airplanes, and even the medical devices we rely on to diagnose and treat illness.

Errors that are introduced in all of this software code can cause major problems down the road for software development organizations, application end users, and society in general. These problems usually relate to security, standards, or defects.

By providing developers with the right tools to detect, understand, and fix problems early, your business can simplify software development, shorten development lifecycles, and improve the quality of software code. The end result is increased innovation, secure applications, and a faster time to market — all at a lower cost.
 

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Time series analysis Auto Arima

Time series analysis is used by many industries in order to extract meaningful statistics, characteristics, and insights. Businesses use time series to improve business performance or mitigate risk in applications such as finance, weather prediction, cell tower capacity planning, pattern recognition, signal processing, and engineering.

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TotalView CUDA

CUDA introduces developers to a number of new concepts (such as kernels, streams, warps, and explicitly multilevel memory) that are not encountered in serial or other parallel programming paradigms. Visibility into these elements is critical for troubleshooting and tuning applications that make use of CUDA. This paper will highlight CUDA concepts implemented in CUDA 3.0 - 4.0, the impact of those concepts for troubleshooting CUDA, and how TotalView helps users deal with these new CUDA-specific constructs. CUDA is frequently used alongside MPI parallelism and host-side multicore and multithread parallelism. The TotalView parallel debugger provides developers with an integrated view of all three levels of parallelism within a single debugging session.

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Transitioning to multicore: Part II

Multicore systems are ubiquitous; it’s virtually impossible to buy even commodity computers without a dual, quad, or hex-core processor. It won’t be long before many-core processors start to be prevalent as well. Each core in a multicore processor is capable of executing a program, so a quad-core processor can run four separate programs at the same time. That’s great if you have many different programs you need to run at one time, but can become a problem when you need performance from a single program. Those four cores can also potentially run one program faster than a single core processor would, but only if the program is written correctly. If you run a sequential (or serial) program written for single core architectures on a multicore platform, it will generally only be able to leverage a single core. Serial programs don’t run any faster, and may even run slightly slower, on multicore processors.

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