There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

This paper presents and characterizes the Princeton Application Repository for Shared-Memory Computers (PARSEC), a benchmark suite for studies of Chip-Multiprocessors (CMPs). Previous available benchmarks for multiprocessors have focused on high-performance computing applications and used a limited number of synchronization methods. PARSEC includes emerging applications in recognition, mining and synthesis (RMS) as well as systems applications which mimic large-scale multithreaded commercial programs. Our characterization shows that the benchmark suite covers a wide spectrum of working sets, locality, data sharing, synchronization and off-chip traffic. The benchmark suite has been made available to the public.

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The PARSEC benchmark suite: characterization and architectural implications

This paper presents and characterizes Rodinia, a benchmark suite for heterogeneous computing. To help architects study emerging platforms such as GPUs (Graphics Processing Units), Rodinia includes applications and kernels which target multi-core CPU and GPU platforms. The choice of applications is inspired by Berkeley's dwarf taxonomy. Our characterization shows that the Rodinia benchmarks cover a wide range of parallel communication patterns, synchronization techniques and power consumption, and has led to some important architectural insight, such as the growing importance of memory-bandwidth limitations and the consequent importance of data layout.

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Rodinia: A benchmark suite for heterogeneous computing

Similarity search finds application in specialized database systems handling complex data such as images or videos, which are typically represented by high-dimensional features and require specific indexing structures. This paper tackles the problem of better utilizing GPUs for this task. While GPUs excel at data-parallel tasks, prior approaches are bottlenecked by algorithms that expose less parallelism, such as k-min selection, or make poor use of the memory hierarchy. 
We propose a design for k-selection that operates at up to 55% of theoretical peak performance, enabling a nearest neighbor implementation that is 8.5x faster than prior GPU state of the art. We apply it in different similarity search scenarios, by proposing optimized design for brute-force, approximate and compressed-domain search based on product quantization. In all these setups, we outperform the state of the art by large margins. Our implementation enables the construction of a high accuracy k-NN graph on 95 million images from the Yfcc100M dataset in 35 minutes, and of a graph connecting 1 billion vectors in less than 12 hours on 4 Maxwell Titan X GPUs. We have open-sourced our approach for the sake of comparison and reproducibility.

Billion-scale similarity search with GPUs

Similarity search finds application in database systems handling complex data such as images or videos, which are typically represented by high-dimensional features and require specific indexing structures. This paper tackles the problem of better utilizing GPUs for this task. While GPUs excel at data parallel tasks such as distance computation, prior approaches in this domain are bottlenecked by algorithms that expose less parallelism, such as $k$ k -min selection, or make poor use of the memory hierarchy. We propose a novel design for $k$ k -selection. We apply it in different similarity search scenarios, by optimizing brute-force, approximate and compressed-domain search based on product quantization. In all these setups, we outperform the state of the art by large margins. Our implementation operates at up to 55 percent of theoretical peak performance, enabling a nearest neighbor implementation that is 8.5 × faster than prior GPU state of the art. It enables the construction of a high accuracy $k$ k -NN graph on 95 million images from the Yfcc100M dataset in 35 minutes, and of a graph connecting 1 billion vectors in less than 12 hours on 4 Maxwell Titan X GPUs. We have open-sourced our approach for the sake of comparison and reproducibility.

Billion-Scale Similarity Search with GPUs

To date, most Internet applications focus on providing information, interaction, and entertainment for humans. However, with the widespread deployment of networked, intelligent sensor technologies, an Internet of Things (IoT) is steadily evolving, much like the Internet decades ago. In the future, hundreds of billions of smart sensors and devices will interact with one another without human intervention, on a Machine-to-Machine (M2M) basis. They will generate an enormous amount of data at an unprecedented scale and resolution, providing humans with information and control of events and objects even in remote physical environments. The scale of the M2M Internet will be several orders of magnitude larger than the existing Internet, posing serious research challenges. This paper will provide an overview of challenges and opportunities presented by this new paradigm.

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Challenges and opportunities of internet of things

Sorting a list of input numbers is one of the most fundamental problems in the field of computer science in general and high-throughput database applications in particular. Although literature abounds with various flavors of sorting algorithms, different architectures call for customized implementations to achieve faster sorting times.This paper presents an efficient implementation and detailed analysis of MergeSort on current CPU architectures. Our SIMD implementation with 128-bit SSE is 3.3X faster than the scalar version. In addition, our algorithm performs an efficient multiway merge, and is not constrained by the memory bandwidth. Our multi-threaded, SIMD implementation sorts 64 million floating point numbers in less than0.5 seconds on a commodity 4-core Intel processor. This measured performance compares favorably with all previously published results.Additionally, the paper demonstrates performance scalability of the proposed sorting algorithm with respect to certain salient architectural features of modern chip multiprocessor (CMP) architectures, including SIMD width and core-count. Based on our analytical models of various architectural configurations, we see excellent scalability of our implementation with SIMD width scaling up to 16X wider than current SSE width of 128-bits, and CMP core-count scaling well beyond 32 cores. Cycle-accurate simulation of Intel's upcoming x86 many-core Larrabee architecture confirms scalability of our proposed algorithm.

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Efficient implementation of sorting on multi-core SIMD CPU architecture

Multimedia applications are becoming increasingly important for a large class of general-purpose processors. Contemporary media applications are highly complex and demand high performance. A distinctive feature of these applications is that they have significant parallelism, including thread- , data-, and instruction-level parallelism, that is potentially well-aligned with the increasing parallelism supported by emerging multi-core architectures. Designing systems to meet the demands of these applications therefore requires a benchmark suite comprising these complex applications and that exposes the parallelism present in them. This paper makes two contributions. First, it presents ALPBench, a publicly available benchmark suite that pulls together five complex media applications from various sources: speech recognition (CMU Sphinx 3), face recognition (CSU), ray tracing (Tachyon), MPEG-2 encode (MSSG), and MPEG-2 decode (MSSG). We have modified the original applications to expose thread-level and data-level parallelism using POSIX threads and sub-word SIMD (Inters SSE2) instructions respectively. Second, the paper provides a performance characterization of the ALPBench benchmarks, with a focus on parallelism. Such a characterization is useful for architects and compiler writers for designing systems and compiler optimizations for these applications.

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The ALPBench benchmark suite for complex multimedia applications

A method and apparatus to support a first peer node receiving an inquiry for data from a second peer node. In one embodiment, the first peer node transcodes the data before transmitting the data to the second peer node, wherein the transcoding includes converting the data into a format that can be processed by the second peer node, and transmitting the data to the second peer node in a transport specification as requested by the second peer node.

Yen-Kuang Chen

Papers

Challenges and opportunities of internet of things

Efficient implementation of sorting on multi-core SIMD CPU architecture

The ALPBench benchmark suite for complex multimedia applications

System for providing a multimedia peer-to-peer computing platform

Implementation of H.264 encoder and decoder on personal computers