Showing papers by "Jatin Chhugani published in 2015"

Can traditional programming bridge the ninja performance gap for parallel computing applications

Abstract: Current processor trends of integrating more cores with wider SIMD units, along with a deeper and complex memory hierarchy, have made it increasingly more challenging to extract performance from applications. It is believed by some that traditional approaches to programming do not apply to these modern processors and hence radical new languages must be discovered. In this paper, we question this thinking and offer evidence in support of traditional programming methods and the performance-vs-programming effort effectiveness of common multi-core processors and upcoming many-core architectures in delivering significant speedup, and close-to-optimal performance for commonly used parallel computing workloads. We first quantify the extent of the "Ninja gap", which is the performance gap between naively written C/C++ code that is parallelism unaware (often serial) and best-optimized code on modern multi-/many-core processors. Using a set of representative throughput computing benchmarks, we show that there is an average Ninja gap of 24X (up to 53X) for a recent 6-core Intel® Core™ i7 X980 Westmere CPU, and that this gap if left unaddressed will inevitably increase. We show how a set of well-known algorithmic changes coupled with advancements in modern compiler technology can bring down the Ninja gap to an average of just 1.3X. These changes typically require low programming effort, as compared to the very high effort in producing Ninja code. We also discuss hardware support for programmability that can reduce the impact of these changes and even further increase programmer productivity. We show equally encouraging results for the upcoming Intel® Many Integrated Core architecture (Intel® MIC) which has more cores and wider SIMD. We thus demonstrate that we can contain the otherwise uncontrolled growth of the Ninja gap and offer a more stable and predictable performance growth over future architectures, offering strong evidence that radical language changes are not required.

Systems and methods for generating virtual contexts

Abstract: Techniques for generated and presenting images of items within user selected context images are presented herein. In an example embodiment, an access module can be configured to receive a first environment image. A simulation module coupled to the access module may process the environment image to identify placement areas within the image, and an imaging module may merge an item image with the environment image and filter the merged image in an erosion area. In various embodiments, the items and environments may be selected by a user and presented to a user in real-time or near-real time as part of an online shopping experience. In further embodiments, the environments may be processed from images taken by a device of the user.

Generating virtual contexts from three dimensional models

Abstract: Techniques for generated and presenting images of items within user selected context images are presented herein. In an example embodiment, an access module can be configured to receive a first environment model and a first wearable item model. A simulation module coupled to the access module may process the environment model to identify placement volumes within the environment model and to place a clothed body model within the placement volume to generate a context model. A rendering module may then generate a context image from the context model. In various embodiments, the environment model used for the context, the wearable item positioned within the environment model, and rendering values used to generate context images may be changed in response to user inputs to generate new context images that are displayed to a user.

Generating and displaying an actual sized interactive object

Abstract: Methods and systems generate and project an actual sized image of an object. An object or a representation of an object may be pre-processed. The pre-processing of a representation of the object may include determining pixels that belong to the object and pixels that belong to a background of the image. The pre-processing may include calculating a bounding box for the object. The projection may include determining an area of projection suitable for the bounding box of the object. The projection may further include projecting an image of intermediate dimensions. The projection may further include up-sampling or down-sampling the image to an actual size. The projection of the actual sized image may be made interactive by rendering at least one additional view of the object.