This ebook is the first authorized digital version of Kernighan and Ritchie's 1988 classic, The C Programming Language (2nd Ed.). One of the best-selling programming books published in the last fifty years, "K&R" has been called everything from the "bible" to "a landmark in computer science" and it has influenced generations of programmers. Available now for all leading ebook platforms, this concise and beautifully written text is a "must-have" reference for every serious programmers digital library.

As modestly described by the authors in the Preface to the First Edition, this "is not an introductory programming manual; it assumes some familiarity with basic programming concepts like variables, assignment statements, loops, and functions. Nonetheless, a novice programmer should be able to read along and pick up the language, although access to a more knowledgeable colleague will help."

The C programming language

Photonic systems for high-performance information processing have attracted renewed interest. Neuromorphic silicon photonics has the potential to integrate processing functions that vastly exceed the capabilities of electronics. We report first observations of a recurrent silicon photonic neural network, in which connections are configured by microring weight banks. A mathematical isomorphism between the silicon photonic circuit and a continuous neural network model is demonstrated through dynamical bifurcation analysis. Exploiting this isomorphism, a simulated 24-node silicon photonic neural network is programmed using “neural compiler” to solve a differential system emulation task. A 294-fold acceleration against a conventional benchmark is predicted. We also propose and derive power consumption analysis for modulator-class neurons that, as opposed to laser-class neurons, are compatible with silicon photonic platforms. At increased scale, Neuromorphic silicon photonics could access new regimes of ultrafast information processing for radio, control, and scientific computing.

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Neuromorphic photonic networks using silicon photonic weight banks.

여성사 쓰기의 가능성과 현주소, 한국여성연구소 여성사연구실 지음, 청년사 1999

Developed for multimedia and game applications, as well as other numerically intensive workloads, the CELL processor provides support both for highly parallel codes, which have high computation and memory requirements, and for scalar codes, which require fast response time and a full-featured programming environment. This first generation CELL processor implements on a single chip a Power Architecture processor with two levels of cache, and eight attached streaming processors with their own local memories and globally coherent DMA engines. In addition to processor-level parallelism, each processing element has a Single Instruction Multiple Data (SIMD) unit that can process from 2 double precision floating points up to 16 bytes per instruction. This paper describes, in the context of a research prototype, several compiler techniques that aim at automatically generating high quality codes over a wide range of heterogeneous parallelism available on the CELL processor. Techniques include compiler-supported branch prediction, compiler-assisted instruction fetch, generation of scalar codes on SIMD units, automatic generation of SIMD codes, and data and code partitioning across the multiple processor elements in the system. Results indicate that significant speedup can be achieved with a high level of support from the compiler.

/pdf/optimizing-compiler-for-the-cell-processor-8bguhih1n2.pdf

Optimizing Compiler for the CELL Processor

Most high-performance software transactional memories (STM) use optimistic invisible reads. Consequently, a transaction might have an inconsistent view of the objects it accesses unless the consistency of the view is validated whenever the view changes. Although all STMs usually detect inconsistencies at commit time, a transaction might never reach this point because an inconsistent view can provoke arbitrary behavior in the application (e.g., enter an infinite loop). In this paper, we formally introduce a lazy snapshot algorithm that verifies at each object access that the view observed by a transaction is consistent. Validating previously accessed objects is not necessary for that, however, it can be used on-demand to prolong the view's validity. We demonstrate both formally and by measurements that the performance of our approach is quite competitive by comparing other STMs with an STM that uses our algorithm.

https://doc.rero.ch/record/18139/files/Riegel_Torvald_-_A_Lazy_Snapshot_Algorithm_with_Eager_Validation_20100430.pdf

A Lazy Snapshot Algorithm with Eager Validation

Software prefetching and locality optimizations are techniques for overcoming the speed gap between processor and memory. In this paper, we evaluate the impact of memory trends on the effectiveness of software prefetching and locality optimizations for three types of applications: regular scientific codes, irregular scientific codes, and pointer-chasing codes. We find for many applications, software prefetching outperforms locality optimizations when there is sufficient memory bandwidth, but locality optimizations outperform software prefetching under bandwidth-limited conditions. The break-even point (for 1 Ghz processors) occurs at roughly 2.5 GBytes/sec on today's memory systems, and will increase on future memory systems. We also study the interactions between software prefetching and locality optimizations when applied in concert. Naively combining the techniques provides robustness to changes in memory bandwidth and latency, but does not yield additional performance gains. We propose and evaluate several algorithms to better integrate software prefetching and locality optimizations, including a modified tiling algorithm, padding for prefetching, and index prefetching.

/pdf/evaluating-the-impact-of-memory-system-performance-on-58sabr7c41.pdf

Evaluating the impact of memory system performance on software prefetching and locality optimizations

Software prefetching and locality optimizations are techniques for overcoming the speed gap between processor and memory. In this paper, we provide a comprehensive summary of current software prefetching and locality optimization techniques, and evaluate the impact of memory trends on the effectiveness of these techniques for three types of applications: regular scientific codes, irregular scientific codes, and pointer-chasing codes. We find that for many applications, software prefetching outperforms locality optimizations when there is sufficient memory bandwidth, but locality optimizations outperform software prefetching under bandwidth-limited conditions. The break-even point (for 1 GHz processors) occurs at roughly 2.26 GBytes/sec on today’s memory systems, and will increase on future memory systems. We also study the interactions between software prefetching and locality optimizations when applied in concert. Naively combining the techniques provides robustness to changes in memory bandwidth and latency, but does not yield additional performance gains. We propose and evaluate several algorithms to better integrate software prefetching and locality optimizations, including a modified tiling algorithm, padding for prefetching, and index prefetching. Finally, we investigate the interactions of stride-based hardware prefetching with our software techniques. We find that combining hardware and software prefetching yields similar performance to software prefetching alone, and that locality optimizations enable stride-based hardware prefetching for benchmarks that do not normally exhibit striding.

/pdf/the-efficacy-of-software-prefetching-and-locality-35zmwxosfj.pdf

The Efficacy of Software Prefetching and Locality Optimizations on Future Memory Systems

Moore's law for traditional electric integrated circuits is facing increasingly more challenges in both physics and economics. Among those challenges is the fact that the bandwidth per compute on the chip is dropping, whereas the energy needed for data movement keeps rising. We benchmark various interconnect technologies, including electrical, photonic, and plasmonic options. We contrast them with hybrid photonic–plasmonic interconnect(s) [HyPPI(s)], where we consider plasmonics for active manipulation devices and photonics for passive propagation integrated circuit elements and further propose another novel hybrid link that utilizes an on-chip laser for intrinsic modulation, thus bypassing electrooptic modulation. Our analysis shows that such hybridization will overcome the shortcomings of both pure photonic and plasmonic links. Furthermore, it shows superiority in a variety of performance parameters such as point-to-point latency, energy efficiency, throughput, energy delay product, crosstalk coupling length, and bit flow density, which is a new metric that we defined to reveal the tradeoff between the footprint and performance. Our proposed HyPPIs show significantly superior performance compared with other links.

The Case for Hybrid Photonic Plasmonic Interconnects (HyPPIs): Low-Latency Energy-and-Area-Efficient On-Chip Interconnects

MorphoNoC: Exploring the Design Space of a Configurable Hybrid NoC using Nanophotonics

Performance modeling is a challenging problem due to the complexities of hardware architectures. In this paper, we present PPT-GPU, a scalable and accurate simulation framework that enables GPU code developers and architects to predict the performance of applications in a fast, and accurate manner on different GPU architectures. PPT-GPU is part of the open source project, Performance Prediction Toolkit (PPT) developed at the Los Alamos National Laboratory. We extend the old GPU model in PPT that predict the runtimes of computational physics codes to offer better prediction accuracy, for which, we add models for different memory hierarchies found in GPUs and latencies for different instructions. To further show the utility of PPT-GPU, we compare our model against real GPU device(s) and the widely used cycle-accurate simulator, GPGPU-Sim using different workloads from RODINIA and Parboil benchmarks. The results indicate that the predicted performance of PPT-GPU is within a 10 percent error compared to the real device(s). In addition, PPT-GPU is highly scalable, where it is up to 450x faster than GPGPU-Sim with more accurate results.

Abdel-Hameed A. Badawy

Papers

Evaluating the impact of memory system performance on software prefetching and locality optimizations

The Efficacy of Software Prefetching and Locality Optimizations on Future Memory Systems

The Case for Hybrid Photonic Plasmonic Interconnects (HyPPIs): Low-Latency Energy-and-Area-Efficient On-Chip Interconnects

MorphoNoC: Exploring the Design Space of a Configurable Hybrid NoC using Nanophotonics

PPT-GPU: Scalable GPU Performance Modeling