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Facilitating High Performance Code Parallelization

Facilitating High Performance Code Parallelization

As mentioned in the introduction, the purpose of this chapter is to use MPI to parallelize an otherwise sequential user’s function call for pattern matching without the user’s intervention. In Chapter 2, we have achieved this for shared memory by using multiple threads. However, with distributed memory, our approach has to be slightly different in the sense that our data now resides on multiple machines, and these machines will each process part of the data. So far, this does not seem like an issue. However, recall that our purpose is to detect pattern matching (which would typically be in the middle of the program), replace the sequential implementation by a parallel one, and then return control to the original user code that will probably use the result of the matching (which will have to be in memory) in some way or another. To integrate the MPI implementation, we will need to execute the MPI application externally from Java, read the system results, and then parse them and send them back to the Java application. The reason for this is that MPI executables cannot be launched without the mpirun command. In addition to launching multiple copies of the MPI executable, mpirun also exports special environment variables to the launched MPI processes. Therefore, calling mpirun is necessary, and in Open MPI there is no way to embed its functionality in the program. It has to be explicitly called. To handle this, we have used the Java exec command. This command executes external
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Language Support for Programming High-Performance Code

Language Support for Programming High-Performance Code

Without using any vectorization techniques, compiling for AVX instead of SSE did not make any notable difference except for blackscholes that ran slightly faster. Surprisingly, auto-vectorization either did not affect the runtime at all or even imposed a performance penalty. The loop vectorizer was never triggered; the SLP vectorizer introduces an overhead for pooling scalars into vectors; finally, the CPU’s SIMD unit might cause slowdowns if it is only barely used. Using Sierra’s 4x vectorization on SSE resulted in a speedup of roughly 2x for volumerender, 2.5x for aobench and mandelbrot, almost 4x for binomial, and about 4.5x for blackscholes. Double-pumping yielded a small improvement most of the time. Using Sierra’s 8x vectorization on AVX resulted in a speedup of roughly 2.5x for volumerender , 3x for aobench, 3.5x for mandelbrot, 4x for binomial, and 7x for blackscholes. We obtained mixed results when double-pumping AVX. We believe this is due to the fact that AVX is internally already double-pumped on Ivy Bridge. Moreover, many AVX instructions still use a native vector length of 4 instead of 8.
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nsCouette – A high-performance code for direct numerical simulations of turbulent Taylor–Couette flow

nsCouette – A high-performance code for direct numerical simulations of turbulent Taylor–Couette flow

We present nsCouette , a highly scalable software tool to solve the Navier–Stokes equations for in- compressible fluid flow between differentially heated and independently rotating, concentric cylinders. It is based on a pseudospectral spatial discretization and dynamic time-stepping. It is implemented in modern Fortran with a hybrid MPI - OpenMP parallelization scheme and thus designed to compute turbulent flows at high Reynolds and Rayleigh numbers. An additional GPU implementation ( C - CUDA ) for intermediate problem sizes and a version for pipe flow ( nsPipe ) are also provided.

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Can High Throughput Atone for High Latency in Compiler-Generated Protocol Code?

Can High Throughput Atone for High Latency in Compiler-Generated Protocol Code?

Contribution & Organization. In this paper, we compare centralized-approach compilation and execution with hybrid-approach compilation and execution. For this, we use nine different connector “families” (i.e., connectors parametric in the number of the coordinated threads), “members” of which Figure 1 shows. Our comparison reveals previously unknown strengths and weaknesses of the approaches under investigation. These new insights are imperative for the future development of our compilation technology and, consequently, for evidencing the performance merits of high-level constructs and abstractions for multicore programming, complementary to their classical software engineering advantages. Although framed in the context of Reo, our technology works at the level of Reo’s formal automaton semantics. This means that we have formulated and implemented our compilers in terms of a general kind of communicating au- tomaton. Therefore, our findings apply to compilation technology not only for Reo but for any high-level model or language whose semantics one can define in terms of such automata (e.g., some process calculi). We expect this generality to make our work interesting to a larger audience, beyond the Reo community.
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SOURCE CODE ANALYSIS AND PERFORMANCE MODELING OF MALWARE

SOURCE CODE ANALYSIS AND PERFORMANCE MODELING OF MALWARE

The exponential growth of malware is degrading the performance of the machines, networks and various wireless devices. The objective of this research is to study those virus’ source codes that primarily affected machines running versions of Microsoft Windows Operating System and analyze them. Our goal is to harden the internet against attacks. All the viruses analyzed in this paper were released in 2003 or years preceding it. Striking similarities were found in the patterns they used to compromise the systems. Also we took a look at the network performance modeling techniques that are being developed and their significance in developing a hardened automated defense system which is able to contain new threats. As will be clear from discussion further, the terms viruses and worms would be used to describe a malicious program, depending on the context, often interchangeably. Such studies will help identify weaknesses in software, develop antivirus system software and harden the internet. The analysis will also aid in computer forensics to determine the sequence of files compromised.
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Morse Code for High Level Security for Cloud Storage

Morse Code for High Level Security for Cloud Storage

The whole world of wireless communications, as we know it today, when Guglielmo Marconi transmitted the Morse code for over a distance of 3 kms by electromagnetic waves. From this time, wireless communications have grown up into a key element of modern society. Electronics devices can exchange information over network by using Wifi. In cloud computing services are ballooning and its multifarious edge makes all the IT industry to migrate from old service model to new on- demand self service model Despite its growing popularity and increasing demand, cloud computing faces security challenges.[2] The security issues are handled by combining cryptography with DNA computing. The DNA cryptographic techniques help the cloud user and provider to protect their sensitive information from unknown access. Cloud computing has huge security risks as it deals with sensitive information. DNA sequence ATGC is the basic sequence of DNA cryptographic technique. The randomness of DNA sequence used to achieve strong encryption algorithms. [4]The DNA computing is high in cost and time consuming. These can be overcome by using modern cryptographic techniques which is based on computer based algorithms. The complex problems can be solved by DNA cryptographic algorithms.
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Performance Tuning of a CFD Code on the Earth Simulator

Performance Tuning of a CFD Code on the Earth Simulator

5. SUMMARY SUMMARY SUMMARY SUMMARY SUMMARY For the DNS of turbulence, the Fourier spectral method has the advantage of accuracy, particularly in terms of solving the Poisson equation, which repre- sents mass conservation and needs to be solved accu- rately for a good resolution of small eddies. However, the method requires frequent execution of the 3D- FFT, the computation of which requires global data transfer. In order to achieve high performance in the DNS of turbulence on the basis of the spectral method, efficient execution of the 3D-FFT is therefore

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Performance prediction for a code with data dependent runtimes

Performance prediction for a code with data dependent runtimes

Fig. 2: Runtime scaling with subsampled images subsampled images. The lowest task-time is by a ‘self-registration,’ that is a registration of an image with itself. This causes the lowest number of itera- tions of EvaluateGradient, causing the min- imum amount work required for a registration in this class. Conversely, a ‘bad brain’ image, which has been empirically found, is much more diffi- cult to correlate and exercises the code closer to the limits of each iteration. Using these upper and lower bounds on the execution time it is possible, by subsampling an image, to hugely shorten the runtime but still keep the salient features of the im- age. When compared with the subsampled worst case registration and subsampled self-registration it is straightforward to identify a candidate work- load parameter. Figure 3 shows how effective this performance prediction technique can be.
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Test Code Quality with Issue Handling Performance

Test Code Quality with Issue Handling Performance

Designing automated tests is a challenging task. One important concern is how to design test fixtures, i.e. code that initializes and configures the system under test so that it is in an appropriate state for running particular automated tests. Test designers may have to choose between writing in-line fixture code for each test or refactor fixture code so that it can be reused for other tests. Deciding on which approach to use is a balancing act, often trading off maintenance overhead with slow test execution. Additionally over time, test code quality can erode and test smells can develop such as the occurrence of overly general fixtures obscure in-line code and dead fields. That test smells related to fixture set-up occurs in industrial projects. Author present a static analysis technique to identify fixture related test smells.
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Implementation and performance of a particle-in-cell code writteninjava

Implementation and performance of a particle-in-cell code writteninjava

Plasma simulation is an important example of a high-performance computing application where computer science issues are of great relevance. In a plasma, each particle, electron or ion, interacts with the external fields and with other particles in ways that can be readily and effectively emulated using object-oriented programming. However, the great cost of plasma simulations has traditionally discouraged object-oriented implementations due to their perceived inferior performance compared with classic procedural FORTRAN or C. In the present paper, we revisit this issue. We have developed a Java particle-in-cell code for plasma simulation, called Parsek. The paper considers different choices for the object orientation and tests their performance. We find that coarse-grained object orientation is faster and practically immune from any degradation compared with a standard procedural implementation (with static classes). The loss in performance for a fine-grained object orientation is a factor of about 50%, which can be almost completely eliminated using advanced Java compilation techniques. The Java code Parsek also provides an interesting realistic application of high-performance computing to compare the performance of Java with FORTRAN. We have conducted a series of tests considering various Java implementations and various FORTRAN implementations. We have also considered different computer architectures and different Java Virtual Machines and FORTRAN compilers. The conclusion is that with Parsek, object-oriented Java can reach CPU speed performances more or less comparable with procedural FORTRAN. This conclusion is remarkable and it is in agreement with the most recent benchmarks, but is at variance with widely held misconceptions about the alleged slowness of Java. Copyright c 2005 John Wiley & Sons, Ltd.
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Combinatorial polarization, code loops, and codes of high level

Combinatorial polarization, code loops, and codes of high level

for 1 ≤ i, j, k ≤ n (cf. the proof of [2, Theorem 5]). It is this construction that turns out to be the most difficult part of the proof that code loops are exactly finite Moufang loops with at most two squares. We simplify and generalize the construction in Section 3. The construction presented here is easier than that of [5] too, because it avoids induction. To conclude our discussion concerning code loops, note that a map f : V → { 0,1 } with combinatorial degree 3 is uniquely specified if we know the values of f (e i ), f (e i + e j ),

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Driving high performance

Driving high performance

goals for individual performance Giving employees regular coaching and feedback on their performance Conducting career development discussions as part of the performance management process Fairly reflecting overall performance in the employee's final performance rating Utilising performance results to determine development plans Differentiating performance between high and low performers

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High Performance Inverter

High Performance Inverter

Function code is used for selecting various functions of FRENIC-Ace. Function code comprises 3 digits or 4 digits of alphanumeric character. The first digit categorizes the group of function code alphabetically and the subsequent 2 or 3 digits identify each code within the group by number. Function code comprises 11 groups: Basic function (F code), Terminal function (E code), Control code (C code), Motor 1 parameter (P code), High-level function (H code) (H1 code), Motor 2 parameter (A code), Application function 1 (J code) (J1 code), Application function 2 (d code), Customizable logic (U code) (U1 code), Link function ( y code), Keypad functions ( k code), and Option function (o code) . The function of each function code is determined according to the data to be set. The following descriptions are for supplementary explanation of function code table. Refer to instruction manual of each option to find the details of the option function (o code).
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Institute of High Performance

Institute of High Performance

different gel film and substrate stiffness factor of NkT/Es and critical chemical potential of thin film gel varying with gel film and substrate stiffness factor of NkT/Es different gel[r]

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High Performance Computing

High Performance Computing

For molecular simulations large-scale compute resources (CPU/GPU) are required with a platform independent modular software architecture, but with software ported and optimized for specific hardware. Software license constraints can be a barrier to the use of HPC so ideally software licenses should either be free or available at low cost. During the discussions there was a concern that drug discovery was being limited to narrow areas of chemical space (and thereby limiting the possibility of new drug discovery) mainly because there were not enough resources available to conduct wider searches using HPC. In order to examine more chemical space to widen the search for drugs, very high power HPC systems are required.
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High Performance Solutions

High Performance Solutions

The global need and consumption of high purity aluminium as well as the applica- tions are steadily increasing. Whether for electrolytic capacitor foils, the semicon- ductor industry, TFT LCD applications, high purity alumina, photovoltaic cells, decorative uses or for electronic storage systems, high purity aluminium is a necessity in all high-tech products. In order to meet the stringent and increasing requirements of high purity aluminium users, Hydro set up the High Purity Business Unit with manufactur- ing facilities in Vigeland (Norway), Grevenbroich (Germany) and a sales and technical support office in Tokyo (Japan). All three locations excel in competence resulting in reliability and defined quality, a prerequisite to meet the extremely high demands of such users.
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High Performance Concrete

High Performance Concrete

The objective of this study is to analyse the performance of Microsilica and Fly Ash in concrete when it is mixed in cement concrete for workability and strength of concrete using OPC 53 grade. Efforts are to improve and develop high performance concrete with the help of past years result which suggests that by the use of cement replacement materials for some determined percentage along with admixtures can help to increase the strength and consistency characteristics of concrete. This study analyzes the performance of concrete mix for compressive strength of cubes at 7 and 28 days and flexural strength of beams for 28 days respectively for M-25 grade concrete. To analyse these properties of concrete, the total investigation was conducted into three groups. The first beam will be a normal beam (Type N); in second beam we replace aggregate by microsilica and fly ash in tension zone (Type NT); in third beam we will replace aggregate in whole beam by fly ash and microsilica (Type NA). Super plasticizer was used to increase the workability at water-cement ratio of 0.43 for all combinations. On the basis of past result the replacement of cement by Microsilica and Fly Ash was found to increase in strength.
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Performance Analysis with High-Level Languages for High-Performance Reconfigurable Computing

Performance Analysis with High-Level Languages for High-Performance Reconfigurable Computing

Performance analysis can be divided into six steps (derived from Maloney’s work on the TAU performance analysis framework for traditional processors [7]) whose end goal is to produce an optimized application. These steps are Instrument, Measure, Execute, Analyze, Present, and Optimize (see Figure 1). The instrumentation step inserts the necessary code (i.e. for additional hardware in the FPGA’s case) to access and record application data at runtime, such as variables or signals to capture performance indicators. Measurement is the process of recording and storing the performance data at runtime while the application is executing. After execution, analysis of performance data to identify potential bottlenecks can be performed in one of two ways. Some tools can automatically analyze the measured data, while other tools rely solely upon the developer to analyze the results. In either case, data is typically presented to the user via text, charts, or other visualizations to allow for further analysis. Finally, optimization is performed by modifying the application’s code based upon insights gained via the previous steps. Since automated optimization is an open area of research, optimization at present is typically a manual process. Finally, these steps may be repeated as
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Code Generation for High-Assurance Java Card Applets

Code Generation for High-Assurance Java Card Applets

Kestrel has been developing tools and techniques for the development of high-assurance software. Our Specware tool [3] provides capabilities to write formal specs, refine them, compose specs and refinements, generate running code from refined specs, and call mechanical theorem provers to prove the correctness of specs and refinements as well as putative properties of specs for validation purposes. Our Planware tool [4] is a domain-specific extension of Specware: it is a fully automatic generator of schedulers from high-level specs written in a simple and intuitive tabular form. Planware internally translates these domain- specific specs into Specware specs and makes use of the Specware machinery to refine them to extremely efficient code. Other generators have been and are being built at Kestrel (e.g. a generator of C code from Stateflow [5] diagrams). Currently, we have projects underway to apply and extend our synthesis technology to Java Card. More precisely, we are working on the following two tasks:
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Test Code Quality With Issue Handling Performance

Test Code Quality With Issue Handling Performance

The current test code quality model is solely based on source code measures. It might be interesting to extend the model with historical information that would bring additional insight as to the number of previous bugs (defects that were not caught by the test code). To assess the relation between test code quality and issue handling performance we used three issue handling indicators. However, other indicators reflect different aspects of issue handling, e.g., the percentage of reopened issues could provide an indication of issue resolution efficiency. Future research that includes additional indicators will contribute to the knowledge of which aspects of issue handling are related to test code quality in particular.
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