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13 results for “Execution time”
MSP430FR5969 Basic Block Worst Case Energy Consumption (WCEC) and Worst Case Execution Time (WCET) dataset
<p>This dataset contains around 30 000 basic blocks whose energy consumption and execution time have been measured in isolation on the MSP430FR5969 microcontroller, at 1MHz. Basic blocks were executed in a worst case scenario regarding the MSP430 FRAM cache and CPU pipeline. The dataset creation process is described thoroughly in [1].</p> <h2>Folder structure</h2> <p>This dataset is composed of the following files:</p> <ul> <li><code>basic_blocks.tar.xz</code> contains all basic blocks (BB) used in the dataset, in a custom JSON format,</li> <li><code>data.csv</code>/<code>data.xlsx</code> contains the measured energy consumption and execution time for each basic block</li> </ul> <p>We first details how the <code>basic_blocks.tar.gz</code> archive is organized, and then present the CSV/XSLX spreadsheet format.</p> <h2>Basic Blocks</h2> <p>We extracted the basic blocks from a subset of programs of the <em>AnghaBench</em> benchmark suite [2]. The <code>basic_blocks.tar.gz</code> archive consist of the extracted basic blocks organized as <code>json</code> files. Each <code>json</code> file correspond to a C source file from <em>AnghaBench</em>, and is given a unique identifier. An example json (<code>137.json</code>) is available here:</p> <div> <pre><code>{ "extr_pfctl_altq.c_pfctl_altq_init": [ # Basic block 1 [ # Instruction 1 of BB1 [ "MOV.W", "#queue_map", "R13" ], # Instruction 2 of BB1 [ "MOV.B", "#0", "R14" ], # Instruction 3 of BB1 [ "CALL", "#hcreate_r", null ] ], # Basic block 2 [ .... ] ] }</code></pre> </div> <p>The json contains a dict with only one key pointing to an array of basic blocks. This key is the name of the original C source file in <em>AnghaBench</em> from which the basic blocks were extracted (here <a href="https://github.com/brenocfg/AnghaBench/blob/master/freebsd/sbin/pfctl/extr_pfctl_altq.c_pfctl_altq_init.c">extr_pfctl_altq.c_pfctl_altq_init.c</a>). The array contains severals basic blocks, which are represented as an array of instructions, which are themselves represented as an array <code>[OPCODE, OPERAND1, OPERAND2]</code>.</p> <p>Then, each basic block can be identified uniquely using two ids : its <em>file id</em> and its <em>offset</em> in the file (<code>id=<file id>_<offset></code>). In our example, the basic block 1 can be identified by the json file id (<code>137</code>) and its offset in the file (<code>0</code>). Its ID is <code>137_0</code>. This ID is used to make the mapping between a basic block and its energy consumption/execution time, with the <code>data.csv</code>/<code>data.xlsx</code> spreadsheet.</p> <h2>Energy Consumption and Execution Time</h2> <p>Energy consumption and execution time data are stored in the <code>data.csv</code> file. Here is the extract of the csv file corresponding to the basic block <code>137_0</code>. The spreadsheet format is described below.</p> <pre><code>bb_id;nb_inst;max_energy;max_time;avg_time;avg_energy;energy_per_inst;nb_samples;unroll_factor 137_0;3;8.77;7.08;7.04;8.21;2.92;40;50</code></pre> <p>Spreadsheet format :</p> <ul> <li><code>bb_id</code>: the unique identifier of a basic block (cf. <a>Basic Blocks</a>)</li> <li><code>nb_inst</code>: the number of instructions in the basic block</li> <li><code>max_energy</code>: the maximum energy comsumption (in nJ) measured during the experiment</li> <li><code>max_time</code>: the maximum execution time (in us) measured during the experiment</li> <li><code>avg_time</code>: the average execution time (in us) measured during the experiment</li> <li><code>avg_energy</code>: the average energy comsumption (in nJ) measured during the experiment</li> <li><code>energy_per_inst</code>: the average energy consumption per instruction (correspond to <code>avg_energy</code>/<code>nb_inst</code>)</li> <li><code>nb_samples</code>: how much time the basic block energy consumption/execution time has been measured</li> <li><code>unroll_factor</code>: how much time the basic block was unrolled (cf Basic Block Unrolling)</li> </ul> <h3>Basic Block Unrolling</h3> <p>To measure the energy consumption and execution time of the msp430, we need to be able to handle the scale difference between the measurement tool and the basic block execution time. This is achieved by duplicating the basic block multiple times while making sure to keep the worst-case memory layout as explained in the paper. The number of time the basic block has been duplicated is called the <code>unroll_factor</code>.</p> <p>Values of energy and time are always given per basic block, so they have already been divided by the unroll factor.</p> <p> </p> <h2>Dataset description</h2> <h3>Features</h3> <p>The selected features after PCA analysis for both energy and time model are listed here: <strong> MOV.W_Rn_Rn, MOV.W_X(Rn)_X(Rn), CALL, MOV.B_#N_Rn, ADD.W_Rn_Rn, MOV.W_@Rn_Rn, MOV.W_X(Rn)_Rn, ADD.W_#N_Rn, PUSHM.W_#N_Rn, MOV.W_X(Rn)_ADDR, CMP.W_#N_Rn, MOV.W_&ADDR_X(Rn), MOV.W_Rn_X(Rn), BIS.W_Rn_Rn, RLAM.W_#N_Rn, SUB.W_#N_Rn, MOV.W_&ADDR_Rn, MOV.W_#N_X(Rn), CMP.W_Rn_Rn, BIT.W_ADDR_Rn, MOV.W_@Rn_X(Rn), ADD.W_#N_X(Rn), MOV.W_#N_Rn, AND.W_Rn_Rn, MOV.W_Rn_ADDR, SUB.W_Rn_Rn, MOV.W_ADDR_Rn, MOV.W_X(Rn)_&ADDR, MOV.W_ADDR_ADDR, JMP, ADD_#N_Rn, BIS.W_Rn_X(Rn), SUB_Rn_Rn, MOV.W_ADDR_X(Rn), ADDC_#N_X(Rn), MOV.B_Rn_Rn, CMP.W_X(Rn)_X(Rn), ADD_Rn_Rn, nb_inst, INV.W_Rn_, NOP__, ADD.W_X(Rn)_X(Rn), ADD.W_Rn_X(Rn), MOV.B_@Rn_Rn, BIS.W_X(Rn)_X(Rn), MOV.B_#N_X(Rn), MOV.W_#N_ADDR, AND.W_#N_ADDR, SUBC_X(Rn)_X(Rn), BIS.W_#N_X(Rn), SUB.W_X(Rn)_X(Rn), AND.B_#N_Rn, ADD_X(Rn)_X(Rn), MOV.W_@Rn_ADDR, MOV.W_&ADDR_ADDR, ADDC_Rn_Rn, AND.W_#N_X(Rn), SUB_#N_Rn, RRUM.W_#N_Rn, AND_ADDR_Rn, CMP.W_X(Rn)_ADDR, MOV.B_#N_ADDR, ADD.W_#N_ADDR, CMP.B_#N_Rn, SXT_Rn_, XOR.W_Rn_Rn, CMP.W_@Rn_Rn, ADD.W_@Rn_Rn, ADD.W_X(Rn)_Rn, AND.W_Rn_X(Rn), CMP.B_Rn_Rn, AND.W_X(Rn)_X(Rn), BIC.W_#N_Rn, BIS.W_#N_Rn, AND.B_#N_X(Rn), MOV.B_X(Rn)_X(Rn), AND.W_@Rn_Rn, MOV.W_#N_&ADDR, BIS.W_Rn_ADDR, SUB.W_X(Rn)_Rn, SUB.W_Rn_X(Rn), SUB_X(Rn)_X(Rn), MOV.B_@Rn_X(Rn), CMP.W_@Rn_X(Rn), ADD.W_X(Rn)_ADDR, CMP.W_Rn_X(Rn), BIS.W_@Rn_X(Rn), CMP.B_X(Rn)_X(Rn), RRC.W_Rn_, MOV.W_@Rn_&ADDR, CMP.W_#N_X(Rn), ADDC_X(Rn)_Rn, CMP.W_X(Rn)_Rn, BIS.W_X(Rn)_Rn, SUB_X(Rn)_Rn, MOV.B_X(Rn)_Rn, MOV.W_ADDR_&ADDR, AND.W_#N_Rn, RLA.W_Rn_, INV.W_X(Rn)_, XOR.W_#N_Rn, SUB.W_Rn_ADDR, BIC.W_#N_X(Rn), MOV.B_X(Rn)_ADDR, ADD_#N_X(Rn), SUB_Rn_X(Rn), MOV.B_&ADDR_Rn, MOV.W_Rn_&ADDR, ADD_X(Rn)_Rn, AND.W_X(Rn)_Rn, PUSHM.A_#N_Rn, RRAM.W_#N_Rn, AND.W_@Rn_X(Rn), BIS.B_Rn_X(Rn), SUB.W_@Rn_Rn, CLRC__, CMP.W_#N_ADDR, XOR.W_Rn_X(Rn), MOV.B_Rn_ADDR, CMP.B_X(Rn)_Rn, BIS.B_Rn_Rn, BIS.W_X(Rn)_ADDR, CMP.B_#N_X(Rn), CMP.W_Rn_ADDR, XOR.W_X(Rn)_Rn, MOV.B_Rn_X(Rn), ADD.B_#N_Rn</strong></p> <h3>Code</h3> <p>The trained machine learning model, tests, and local explanation code can be generated and found here: <a href="https://colab.research.google.com/drive/1zaCIkbKDRDtwzhY-r3Iff0OpOSkOUibW?usp=sharing" target="_blank" rel="noopener">WORTEX Machine learning code</a> </p> <h2>Acknowledgment</h2> <p>This work has received a French government support granted to the Labex CominLabs excellence laboratory and managed by the National Research Agency in the “Investing for the Future” program under reference ANR-10-LABX-07-01</p> <h2>Licensing</h2> <p>Copyright 2024 Hector Chabot Copyright 2024 Abderaouf Nassim Amalou Copyright 2024 Hugo Reymond Copyright 2024 Isabelle Puaut</p> <p>Licensed under the Creative Commons Attribution 4.0 International License</p> <h2>References</h2> <p>[1] Reymond, H., Amalou, A. N., Puaut, I. “WORTEX: Worst-Case Execution Time and Energy Estimation in Low-Power Microprocessors using Explainable ML” in 22nd International Workshop on Worst-Case Execution Time Analysis (WCET 2024) </p> <p>[2] Da Silva, Anderson Faustino, et al. “Anghabench: A suite with one million compilable C benchmarks for code-size reduction.” 2021 IEEE/ACM International Symposium on Code Generation and Optimization (CGO). IEEE, 2021.</p>
Dorsal striatum coding for the timely execution of action sequences
<p>The automatic initiation of actions can be highly functional. But occasionally these actions cannot be withheld and are released at inappropriate times, impulsively. Striatal activity has been shown to participate in the timing of action sequence initiation and it has been linked to impulsivity. Using a self-initiated task, we trained adult male rats to withhold a rewarded action sequence until a waiting time interval has elapsed. By analyzing neuronal activity we show that the striatal response preceding the initiation of the learned sequence is strongly modulated by the time subjects wait before eliciting the sequence. Interestingly, the modulation is steeper in adolescent rats, which show a strong prevalence of impulsive responses compared to adults. We hypothesize this anticipatory striatal activity reflects the animals' subjective reward expectation, based on the elapsed waiting time, while the steeper waiting modulation in adolescence reflects age-related differences in temporal discounting, internal urgency states, or explore-exploit balance. </p>
BRAIN Journal-Performance Analysis of Unsupervised Clustering Methods for Brain Tumor Segmentation-Figure 4.Performance based on no. of tumor pixel & execution time
<p>In this paper we segmented the brain tumors in axial view of MR images with the help of<br> unsupervised clustering method i.e. K-means clustering. The unsupervised clustering methods gave<br> the better results than traditional method.<br> The performance analysis and comparison is done f on the basis of no. of tumor pixels in<br> segmented brain tumor and the execution time for the same. Regarding the no. of tumor pixels, Kmeans<br> clustering gave a better result than the other methods. The clustering algorithms were tested<br> with a data base of 20 MRI brain images. K-means clustering achieved almost 90%result</p>
Dataset of execution times of programs compiled under different GCC optimizaion levels (-O0, -O1, -O2, -O3) on the Xilinx Zynq UltraScale+ MPSoC ZCU104 (Cortex A53)
<p>This dataset contains execution time measurements for a set of programs from the TACLe benchmark suite, including <em>lift</em>, <em>powerwindow</em>, <em>bsort</em>, <em>matrix1</em>, <em>epic</em>, <em>dijkstra</em>, mpeg2 and <em>quicksort</em>.</p> <p>The data was collected using RT-Bench, an open-source framework designed to execute these benchmarks periodically in a real-time context.</p> <p>Each program was compiled using five different GCC optimization levels (-O0, -O1, -O2, -O3, and -Ofast) and executed periodically 1000 times.</p> <p>The experiments were conducted on the Zynq ZCU104 platform, which features a quad-core Arm Cortex A53 processor, running the Ubuntu 22.04 LTS arm64 OS with the 5.15.0-1015-xilinx-zynqmp kernel.</p> <p>This dataset is published at <a href="https://2024.ieee-etfa.org/" target="_blank" rel="noopener">ETFA</a> 2024 as part of a work-in-progress paper called "<a href="https://hal.science/hal-04667091/" target="_blank" rel="noopener">Impact of compilation optimization levels on execution time variability</a>"</p>
Dorsal striatum coding for the timely execution of action sequences
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Energy consumption, execution time and fail requests rate of a proactive energy-aware auto-scaling solution for edge-based infrastructures applied to real-world workload.
<p>Spreadsheet of the results obtained with our horizontal auto-scaling proposal presented in "A proactive energy-aware auto-scaling solution for edge-based infrastructures". In that research, we present a proactive horizontal auto-scaling framework for edge infrastructures, which considers both the base (idle) and dynamic (due to application execution) energy consumption of edge nodes and the node scaling mechanism. Simulations were performed with the EdgeCloudSim simulator with a workload provided by Shanghai Telecom and the results show up to a 92.5% decrease in energy consumption, a failed request rate of up to 0%, and reasonable execution times of the auto-scaling process for different problem sizes.</p> <p>Proactive auto-scaling mechanisms in edge-based infrastructures can anticipate user service requests by allocating computing resources while supporting the quality of service needed by a vast range of applications requiring, e.g., a low latency or response time. </p> <p>This work is supported by the European Union's H2020 research and innovation program under grant agreement DAEMON 101017109 and by the projects co-financed by FEDER funds LEIA UMA18-FEDERJA-15, MEDEA RTI2018-099213-B-I00 (MCI/AEI) and RHEA P18-FR-1081.</p>
Execution times to find strong mutant
<p>A study by Gizzo et al. [1] found that the number of mutants was an inaccurate measure of real computational cost, and that execution times should be used instead. Taking into account this research, the execution time needed by each algorithm to find the S% strong mutants is measured.</p> <p>The document includes a list of runtimes (nanoseconds) for each test case: BruteForce, Ecological Island, SnifferCongestion, and Terminal. At the bottom of each S% for all the algorithms the mean is calculated. Moreover, for each test case, a table with a comparison of the calculated mean is displayed. For each S% strong mutant, the lowest runtime is highlighted.</p> <p>[1] Guizzo G, Sarro F, Harman M (2020) Cost measures matter for mutation testing study validity. In: Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, Association for Computing Machinery, New York, NY, USA, ESEC/FSE 2020, p 1127–1139, URL https://doi.org/10.1145/3368089.3409742</p>
Pre-training and fine-tuning dataset for transformers consisting of basic blocks and their execution times (average, minimum, and maximum) along with the execution context of these blocks, for various Cortex processors M7, M4, A53, and A72.
<p>We are making public the dataset used for training CAWET, a tool for estimating the Worst-Case Execution Time (WCET) of basic blocks using the Transformer XL model. CAWET leverages the Transformer architecture for accurate WCET predictions, and its training involves two main phases: self-supervised pre-training and fine-tuning.</p><p>CAWET undergoes a pre-training process on a substantial corpus of basic blocks to enable the Transformer to grasp the intricacies of the assembly language in focus. For this, we utilized CodeNet \cite{codenet}, a comprehensive collection of publicly submitted solutions to competitive programming challenges, comprising roughly 900,000 C programs. These programs were cross-compiled to the target architecture and subsequently disassembled using GNU binary utilities with objdump. The textual output from objdump, post a series of basic parsing operations (e.g., address extraction, separation of basic blocks), serves as the foundation for an extensive pre-training dataset. We employed this dataset to develop a vocabulary model utilizing sentence piece \cite{sentencepiece}. Following the completion of the sentence piece model's training, it becomes ready for use in tokenizing any binary programs written in the target instruction set.</p><p>The fine-tuning phase of CAWET involves its adaptation to basic blocks along with their contextual information. Here, we used a varied and openly accessible collection of programs, namely, The Algorithms (accessible at: <a href="https://github.com/TheAlgorithms/C">https://github.com/TheAlgorithms/C</a>), MiBench \cite{mibench}, and Polybench \cite{polybench}.</p><p>The provided zip file encompasses the following directories:</p><p>Fine_Tuning: This includes four distinct files, each tailored for a specific processor: Cortex_M4, Cortex_M7, Cortex_A53, and Cortex_72. Each file encompasses the basic block under analysis (bbUA), the preceding 10 basic blocks executed prior to it, and timing information related to the bbUA (mean, min, max, normalization, etc.).</p><p>Pre_Training: This comprises two extensive files, dataset_CortexA and dataset_CortexM, utilized for pre-training the transformers for the Masked Language Modeling Task (MLM). Additionally, it includes the sentence piece model and the necessary code to facilitate accurate tokenization.</p><p>For additional information, please refer to the CAWET paper or contact us at <a href="mailto:ea_amalou@esi.dz">ea_amalou@esi.dz</a></p><p> </p><p>Citation:</p><p>@inproceedings{amalou2023cawet,</p><p> title={CAWET: Context-Aware Worst-Case Execution Time Estimation Using Transformers},</p><p> author={Amalou, Abderaouf N and Fromont, Elisa and Puaut, Isabelle},</p><p> booktitle={35th Euromicro Conference on Real-Time Systems (ECRTS 2023)},</p><p> year={2023},</p><p> organization={Schloss Dagstuhl-Leibniz-Zentrum f{\"u}r Informatik}</p><p>}</p><p> </p><p><strong>Bibliography</strong>:</p><p>codenet</p><p>@article{codenet2021,</p><p> title={CodeNet: A large-scale AI for code dataset for learning a diversity of coding tasks},</p><p> author={Puri, Ruchir and Kung, David S and Janssen, Geert and Zhang, Wei and Domeniconi, Giacomo and Zolotov, Vladimir and Dolby, Julian and Chen, Jie and Choudhury, Mihir and Decker, Lindsey and others},</p><p> journal={arXiv preprint arXiv:2105.12655},</p><p> year={2021}</p><p>}</p><p>sentencepiece</p><p>@article{sentencepiece2018,</p><p> title={Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing},</p><p> author={Kudo, Taku and Richardson, John},</p><p> journal={arXiv preprint arXiv:1808.06226},</p><p> year={2018}</p><p>}</p><p>mibench</p><p>@inproceedings{polybench2014,</p><p> title={Understanding polybench/c 3.2 kernels},</p><p> author={Yuki, Tomofumi},</p><p> booktitle={International workshop on polyhedral compilation techniques (IMPACT)},</p><p> pages={1--5},</p><p> year={2014}</p><p>}</p><p>polybench: </p><p>@inproceedings{mibench,</p><p> title={MiBench: A free, commercially representative embedded benchmark suite},</p><p> author={Guthaus, Matthew R and Ringenberg, Jeffrey S and Ernst, Dan and Austin, Todd M and Mudge, Trevor and Brown, Richard B},</p><p> booktitle={4th IEEE international workshop on workload characterization},</p><p> year={2001}</p><p>}</p>
Predicting the execution time of COSMO weather forecast models
<p>This data set is the work of E. Di Giacomo Master Thesis at the University of Bologna.</p> <p>Predicting the execution time of a numerical weather forecast model is a complex task. Generally, these models simulate the evolution of atmospheric weather and they are typically used for the production of weather forecasts, one or multiple times a day. Given their computational complexity, they require large computing capabilities, such as High Performance Computing systems. In these systems, job scheduling and resource allocation are carefully managed to optimize the usage of the finite and expensive hardware resources; in particular, several allocation and related pricing decisions are based on estimates of the duration of the application submitted, such as the execution time of weather forecast models.<br> A reliable prediction of execution time allows for a better management of the overall system, an improved planning of the model execution, as well as the identification of possible anomalies during the execution, thus providing great benefits to both system administrators and users.</p> <p>This data set regards a particular weather forecast model, namely the COSMO model, the weather forecasting model used at the the Hydro-Meteo-Climate Structure of Arpae Emilia-Romagna. The data set contains many execution times of the COSMO meteorological model run under a variety of different scientific parameters and parallelization levels.</p>
Executive Reaction Time Test in Assessment of Cognitive Dysfunction After Aortic Valve Procedures
ClinicalTrials.gov study NCT01953068. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Comparing Transversalis Fascia Plane Block and Quadratus Lumborum Block for Post-Cesarean Pain Management: Efficacy, Execution Time, and Practicality
ClinicalTrials.gov study NCT06874569. IPD Sharing: NO. Countries: 1. Publications: 3.
The Relationship Between Reaction Time and Executive Functions in Female Athletes
ClinicalTrials.gov study NCT05789472. IPD Sharing: Not stated. Countries: 1. Publications: 0.
QC_execution_time
<p>Quantum Chemistry execution time for PSI4</p>
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