Financial Forecasting Using Evolutionary Computational Techniques

Financial forecasting or specially stock market prediction is one of the hottest field of research lately due to its commercial applications owing to high stakes and the kinds of attractive benefits that it has to offer. In this project we have analyzed various evolutionary computation algorithms for forecasting of financial data. The financial data has been taken from a large database and has been based on the stock prices in leading stock exchanges .We have based our models on data taken from Bombay Stock Exchange (BSE), S&P500 (Standard and Poor’s) and Dow Jones Industrial Average (DJIA). We have designed three models and compared those using historical data from the three stock exchanges. The models used were based on: 1. Radial Basis Function parameters updated by Particle swarm optimization. 2. Radial Basis Function parameters updated by Least Mean Square Algorithm. 3. FLANN parameters updated by Particle Swarm optimization. The raw input for the experiment is the historical daily open, close, high, low and volume of the concerned index. However the actual input to the model was the parameters derived from these data. The results of the experiment have been depicted with the aid of suitable curves where a comparative analysis of the various models is done on the basis on various parameters including error convergence and the Mean Average Percentage Error (MAPE). Key Words: Radial Basis Functions, FLANN, PSO, LM

Asynchronous Automata Processing on GPUs

Finite-state automata serve as compute kernels for many application domains such as pattern matching and data analytics. Existing approaches on GPUs exploit three levels of parallelism in automata processing tasks: 1)~input stream level, 2)~automaton-level and 3)~state-level. Among these, only state-level parallelism is intrinsic to automata while the other two levels of parallelism depend on the number of automata and input streams to be processed. As GPU resources increase, a parallelism-limited automata processing task can underutilize GPU compute resources. To this end, we propose AsyncAP, a low-overhead approach that optimizes for both scalability and throughput. Our insight is that most automata processing tasks have an additional source of parallelism originating from the input symbols which has not been leveraged before. Making the matching process associated with the automata tasks asynchronous, i.e., parallel GPU threads start processing an input stream from different input locations instead of processing it serially, improves throughput significantly and scales with input length. When the task does not have enough parallelism to utilize all the GPU cores, detailed evaluation across 12 evaluated applications shows that AsyncAP achieves up to 58× speedup on average over the state-of-the-art GPU automata processing engine. When the tasks have enough parallelism to utilize GPU cores, AsyncAP still achieves 2.4× speedup.</jats:p

Path Forward Beyond Simulators: Fast and Accurate GPU Execution Time Prediction for DNN Workloads

This is the artifact for the paper "Path Forward Beyond Simulators: Fast and Accurate GPU Execution Time Prediction for DNN Workloads" to appear in MICRO 2023

Asynchronous Automata Processing on GPUs

Finite-state automata serve as compute kernels for many application domains such as pattern matching and data analytics. Existing approaches on GPUs exploit three levels of parallelism in automata processing tasks: 1)∼input stream level, 2)∼automaton-level and 3)∼state-level. Among these, only state-level parallelism is intrinsic to automata while the other two levels of parallelism depend on the number of automata and input streams to be processed. As GPU resources increase, a parallelism-limited automata processing task can underutilize GPU compute resources. To this end, we propose AsyncAP, a low-overhead approach that optimizes for both scalability and throughput. Our insight is that most automata processing tasks have an additional source of parallelism originating from the input symbols which has not been leveraged before. Making the matching process associated with the automata tasks asynchronous, i.e., parallel GPU threads start processing an input stream from different input locations instead of processing it serially, improves throughput significantly and scales with input length. When the task does not have enough parallelism to utilize all the GPU cores, detailed evaluation across 12 evaluated applications shows that AsyncAP achieves up to 2.4× speedup on average over the state-of-the-art GPU automata processing engine. When the tasks have enough parallelism to utilize GPU cores, AsyncAP still achieves 2.4× speedup. © 2023 Owner/Author

Asynchronous Automata Processing on GPUs

Finite-state automata serve as compute kernels for application domains such as pattern matching and data analytics. Existing approaches on GPUs exploit three levels of parallelism in automata processing tasks: 1) input stream level, 2) automaton-level, and 3) state-level. Among these, only state-level parallelism is intrinsic to automata while the other two levels of parallelism depend on the number of automata and input streams to be processed. As GPU resources increase, a parallelism-limited automata processing task can underutilize GPU compute resources. To overcome this, we propose AsyncAP, a low-overhead approach that optimizes scalability and throughput. Our insight is that most automata processing tasks have an additional source of parallelism originating from the input symbols which has not been leveraged before. By making the matching process asynchronous, which involves having parallel GPU threads process an input stream from different input locations instead of processing it serially, AsyncAP is able to significantly improve throughput and scale with input length. Detailed evaluation across 12 applications shows that AsyncAP achieves an average speedup of 58x speedup over the state-of-the-art GPU automata processing engine when the task does not have enough parallelism to utilize all GPU cores. When tasks have enough parallelism to utilize GPU cores, AsyncAP still achieves 2.4x speedup. © 2023 Owner/Author