This paper characterizes the memory behavior on emerging RMS (recognition, mining, and synthesis) workloads for future multi-core processors. As multi-core processors proliferate across different application domains, and the number of on-die cores continues to increase, a key issue facing processor architects is the design of the on-die last level cache (LLC). In this paper, we explore the LLC design space for multi-threaded RMS workloads by examining the working set sizes, data sharing behavior, and spatial data locality. Our study reveals that these RMS workloads are memory intensive, have large working-set sizes greater than 16MB on average, exhibit a significant amount of data sharing, about47% on average, and show strong strided streaming access behavior with 77% of accesses in regular pattern. Based on the observations, we then investigate the potential cache architecture choices for future multi-core design. Our experiments show that for these workloads (a) large DRAM caches can be useful to address their large working sets; E.g., a 128MB DRAM cache can reduce the average L1 miss penalty by 18%; (b) shared last level cache provides better cache performance than private cache; E.g., a 8MB shared cache provides 25% performance improvement over a private one with the same total size; and (c) stride based hardware prefetcher provides significant performance benefit by 25%. As a result, we suggest a memory hierarchy with a 128MB DRAM cache, a 8MB on-die SRAM shared cache and an 8-entry stride prefetcher to accommodate RMS workloads.

Understanding the Memory Behavior of Emerging Multi-core Workloads

Emerging video-mining applications such as image and video retrieval and indexing will require real-time processing capabilities. A many-core architecture with 64 small, in-order, general-purpose cores as the accelerator can help meet the necessary performance goals and requirements. The key video-mining modules can achieve parallel speedups of 19times to 62times from 64 cores and get an extra 2.3times speedup from 128-bit SIMD vectorization on the proposed architecture.

Accelerating Video-Mining Applications Using Many Small, General-Purpose Cores

This paper studies how to parallelize the emerging media mining workloads on existing small-scale multi-core processors and future large-scale platforms. Media mining is an emerging technology to extract meaningful knowledge from large amounts of multimedia data, aiming at helping end users search, browse, and manage multimedia data. Many of the media mining applications are very complicated and require a huge amount of computing power. The advent of multi-core architectures provides the acceleration opportunity for media mining. However, to efficiently utilize the multi-core processors, we must effectively execute many threads at the same time. In this paper, we present how to explore the multi-core processors to speed up the computation-intensive media mining applications. We first parallelize two media mining applications by extracting the coarse-grained parallelism and evaluate their parallel speedups on a small-scale multi-core system. Our experiment shows that the coarse-grained parallelization achieves good scaling performance, but not perfect. When examining the memory requirements, we find that these coarse-grained parallelized workloads expose high memory demand. Their working set sizes increase almost linearly with the degree of parallelism, and the instantaneous memory bandwidth usage prevents them from perfect scalability on the 8-core machine. To avoid the memory bandwidth bottleneck, we turn to exploit the fine-grained parallelism and evaluate the parallel performance on the 8-core machine and a simulated 64-core processor. Experimental data show that the fine-grained parallelization demonstrates much lower memory requirements than the coarse-grained one, but exhibits significant read-write data sharing behavior. Therefore, the expensive inter-thread communication limits the parallel speedup on the 8-core machine, while excellent speedup is observed on the large-scale processor as fast core-to-core communication is provided via a shared cache. Our study suggests that (1) extracting the coarse-grained parallelism scales well on small-scale platforms, but poorly on large-scale system; (2) exploiting the fine-grained parallelism is suitable to realize the power of large-scale platforms; (3) future many-core chips can provide shared cache and sufficient on-chip interconnect bandwidth to enable efficient inter-core communication for applications with significant amounts of shared data. In short, this work demonstrates proper parallelization techniques are critical to the performance of multi-core processors. We also demonstrate that one of the important factors in parallelization is the performance analysis. The parallelization principles, practice, and performance analysis methodology presented in this paper are also useful for everyone to exploit the thread-level parallelism in their applications.

Parallelization Strategies and Performance Analysis of Media Mining Applications on Multi-Core Processors

In this paper a mapped-template logic-based parallel optoelectronic processor is proposed for expert systems in order to achieve high speed and high performance for massively parallel processing of rule-based systems. The proposed system is implemented using two-dimensional space optics symbolic correlator. Also in this paper, an optoelectronic system for a parallel inference engine is presented. The proposed optical inference engine infers conclusions to queries by representing knowledge (facts and rules) in 2D space using a matrix-like formulation. Performance evaluation algorithms for both search and reasoning are presented. Performance enhancement technique is also discussed in this paper. The performance analysis of the proposed system shows its remarkably improved performance compared with multiprocessor electronic systems using conventional programming.

Performance of an optoelectronic expert system for massively parallel knowledge base applications

This paper characterizes the sharing behavior of emerging parallelmedia mining workloads for chip-multiprocessors. Media mining refers to techniqueswhereby users retrieve, organize, and manage media data. These applicationsare important in defining the design and performance decisions of futureprocessors. We first show that the sharing behaviors of these workloads have acommon pattern that the shared data footprint is small but the sharing activity issignificant. Less than 15% of the cache space is shared, while 40% to 90% accessesare to the shared footprint in some workloads. Then, we show that forworkloads with such significant sharing activity, a shared last-level cache is moreattractive than private configurations. A shared 32MB last-level cache outperformsa private cache configuration by 20-60%. Finally, we show that in orderto have good scalability on shared caches, thread-local storage should be minimizedwhen building parallel media mining workloads.

Data sharing analysis of emerging parallel media mining workloads

With the exponential increase in media data on personal computers and the Internet, it is critical for end users to efficiently manage metadata to find the information they are looking for. Media mining refers to a technique whereby a user can retrieve, organize, and manage media data. However, most media-mining applications are compute intensive, and they require tera-operations per second. This paper focuses on how tera-scale computing enables new usage models with media-mining techniques. Several representative media-mining usage examples are explored in detail. First, we look at how these new usage models are enabled by a different kind of parallelism. For maximum performance, we provide a general parallel framework to abstract various parallelisms. We also present a detailed architectural performance analysis of several representative workloads on a dual-socket, quad-core system and on a 32-core Chip Multiprocessor (CMP) simulator. The results indicate that these media-mining applications have no obvious limits on concurrency and are amenable to future large-scale, multi-core architectures. They can take full advantage of tera-scale computing power in the form of thread-level parallelism to meet users’ needs. Because the underlying techniques and fundamental algorithms in media mining are widely used in other applications, many of our findings are applicable to other emerging applications as well. INTRODUCTION Rapid advances in the hardware technology of media capture, storage, and computation power have contributed to an amazing growth in digital media content. As content generation and dissemination grows, extracting meaningful knowledge from large amounts of multimedia data becomes increasingly important. Media mining is a kind of technology that helps end users search, browse, and manage large amounts of multimedia data [1]. It yields a wide range of emerging applications with various mass-market segments, e.g., image/video retrieval, video summarization, scene understanding, visual surveillance, digital home entertainment, smart health care, etc. Most of these applications are very complicated and have real-time or even super-real-time processing demands, which require tera-scale computing power to make them usable. In this paper, we present several media-mining applications that require target architectures capable of delivering tera-scale computing. Our study shows that today’s single-core processor system performance is 10x–1000x slower for acceptable human interactions. To accelerate these compute-intensive applications, we exploit the inherent data and function parallelism of these workloads. Our experiments show that with proper parallelization, these workloads can scale well, achieving a speedup of up to 7.5x on a 2-socket, quad-core machine and a speedup of up to 30x on a 32-core CMP simulator. Intel Technology Journal, Volume 11, Issue 3, 2007 Media Mining—Emerging Tera-scale Computing Applications 240 This paper is organized as follows. First, we explore several media-mining usage models and their key techniques. Next, we present several different parallel schemes and a general parallel video-mining framework. Then, we show our performance analysis results of the parallelized workloads. MEDIA-MINING APPLICATIONS Media mining has a huge number of emerging applications with different usage models. We highlight three typical usage models developed at Intel. Media-Mining Usage Models • Sports video analysis: Broadcast sports videos are very popular on television. Using highlights detection, consumers can quickly retrieve specific video clips without having to browse through the whole video. Sports video analytics can be viewed from the perspective of an editor. Based on a predefined semantic intention, an editor combines certain multimedia content elements and their temporal layout to achieve the desired highlighted events. Hence, detecting highlighted events is similar to a reverse process of authoring. The system framework consists of three levels: low-level audio/visual feature extraction, mid-level semantic keywords generation, and high-level event detection [8]. To minimize the semantic gap between low-level features and high-level events, we use mid-level semantic “keywords” followed by a classifier to infer events of interest. Our sports video analysis system can work with a multitude of sports including soccer, hockey, badminton, tennis, and diving. Given a video in a specific domain with predefined semantic intentions, the system can extract the desired events and features and interpret a summarization output video in terms of high-level semantics. • Personal video editing: Home videos are increasingly popular as digital video cameras become more user friendly and portable. However, because home videos for the most part are shot by amateurs, shaking, blurring, under-exposure artifacts, and redundant content are always present. Therefore, the demand for an automated home video editing system [2] is high. Such a system has to be able to recognize how many people and how many scenes are involved, mine the relationship between various people and scenes, and synthesize a short artistic video clip from a long raw video. A typical personal video editing system includes three key modules: intelligent analysis, adaptive selection, and seamless composition. The first module extracts the multi-modal and multi-level audio-visual features; the second module selects the most interesting, important, and informative content; and the third module produces a near-professional story with incidental music. The overall automated home video editing system must be easily extended to the personal video recorder and digital home entertainment system. • Personal video retrieval: A personal video retrieval system is a desktop application that works much like the Google desktop search to help end users manage more and more personal multimedia data from all kinds of mobility digital camera devices. In response to a user query, the personal video retrieval application finds the relevant video clips from a large video database such as from movies, TV, sports games, and home videos. Generally, a retrieval system first extracts low-level audio/visual features from videos, and then detects semantic concepts (keywords) to represent the video content. Finally, a query engine returns retrieval results based on the user’s query and on a similarity model. The query can be text keywords, image examples, hand-drawn sketches, or short video clips, and the output is relevant video clips ranked not only by their content similarity to the query, but also by their importance, according to a concept-link relationship analysis. To gradually improve system performance during the query procedure, the system provides user-friendly relevant feedback and active learning modules. Key Media-Mining Techniques Although the above usage models are quite different from one another, the underlying technologies are common and can be extended to a broad range of media-mining applications. In this paper, four key techniques are extracted from previous usage models to show how media-mining applications are built. • Sports keyword detection: The mid-level module generates semantic “keywords” from the previously described low-level extraction. Listed below are some keywords in sports video analysis. These keywords are used as input for high-level event detection. View type: Based on color histograms of each frame, we can obtain the dominant color to segment the playing field region. We then classify each frame as a global view, medium view, close-up view, and out of view [5]. Play-field: A Hough transform from digital image processing is used to detect field boundaries and penalty box sections. Then a decision-tree-based classifier determines the play Intel Technology Journal, Volume 11, Issue 3, 2007 Media Mining—Emerging Tera-scale Computing Applications 241 position according to the slope and position of the lines. Replay: In broadcast sports videos, to capture clues for significant events, there typically is a replay following an important event. At the beginning and end of each replay, there is generally a logo flying in high speed. We detect logos to identify replays by discovering repeat video segments through dynamic programming [6]. Audio keywords: There are two types of audio keywords: commentator’s excited speech and referee’s whistle: these have a strong correlation to key events in the game such as a foul, a goal, or player entanglements. A Gauss Mixture Model (GMM) is used to detect keywords from low-level audio features including Mel frequency Cepstral coefficients (MFCC), energy, and pitch [7]. • Human detection and tracking: Human detection and tracking is a significant and challenging task in many application scenarios. Different from rigid objects, humans are articulated and jointed by several human-parts, which may lead to pose variance, self-occlusion, etc. In human detection, the first problem is to select the proper features to characterize human regions/parts: Haar wavelets [3] and orientation histograms are mostly used to do this. The second problem with human detection is to use a discriminator to determine whether there are humans and where they are if they are present. The Boosting learning-based detector is preferred [3]. It is an aggressive learning algorithm that produces a strong classifier by choosing features in a family of simple classifiers and combining them linearly. Then a cascaded structure is introduced in order to quickly reject the background regions. Human tracking is essentially finding body regions or parts that correspond with successive frames by using data association and occlusion inference techniques. • Face detection and tracking: Face detection and face tracking have been an important technology and pre-requirement for many person-analysis relevant applications, such as face recognition/identificat

Media Mining Emerging Terascale Computing Applications

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Media Mining Emerging Terascale Computing Applications