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An Affordable Multi-User High-Performance Computing Environment
Explore the variation of hardware available at ETH via several choices of general-purpose computing capable (not only graphics) of our GPU station to produce a high computation environment with minimal cost. More importantly, our system will be recommended to facilitate computational tasks of research projects of faculty members and students.
Keywords: Windows skills, algorithm complexity, GPU performance, big data, high performance
The purpose of this study is to develop a high-performance computation machine with minimal cost. Different algorithms will be evaluated with and without parallelism on different configurations. We will also test the computational performance for a single user and multi-users, locally or remotely, and adding user management and job scheduler.
The purpose of this study is to develop a high-performance computation machine with minimal cost. Different algorithms will be evaluated with and without parallelism on different configurations. We will also test the computational performance for a single user and multi-users, locally or remotely, and adding user management and job scheduler.
- Exploration of the features and capabilities of various tools (API / Library / Applications)
- Run different algorithms (simple and complex) on different hardware configurations.
- Testing CPU/GPU numerical computation performance of integer and floating points operations
- Measure the speed performance of using two GPUs in combination with a CPU.
**Tasks**
- Literature review (5%)
- Run codes sequentially and in parallel (30%)
- Design software for testing exaction performance (30%)
- Test, compare and evaluate results from CPU, one GPU, and two GPUs (30%)
- Report and present results (5%)
**Your Profile**
- Background in Computer Science, Software Engineering, Biostatistics, or related fields.
- Prior experience with programming (Matlab or Python).
- Windows skills required.
- Experience with GPU computing is a plus.
- Able to work independently, pay attention to detail, and deliver results remotely.
- Can visualize data effectively using different charts such as boxplot and scatter plots.
- Background in statistics, time series analysis, and machine learning is needed.
- Exploration of the features and capabilities of various tools (API / Library / Applications) - Run different algorithms (simple and complex) on different hardware configurations. - Testing CPU/GPU numerical computation performance of integer and floating points operations - Measure the speed performance of using two GPUs in combination with a CPU.
**Tasks**
- Literature review (5%) - Run codes sequentially and in parallel (30%) - Design software for testing exaction performance (30%) - Test, compare and evaluate results from CPU, one GPU, and two GPUs (30%) - Report and present results (5%)
**Your Profile**
- Background in Computer Science, Software Engineering, Biostatistics, or related fields. - Prior experience with programming (Matlab or Python). - Windows skills required. - Experience with GPU computing is a plus. - Able to work independently, pay attention to detail, and deliver results remotely. - Can visualize data effectively using different charts such as boxplot and scatter plots. - Background in statistics, time series analysis, and machine learning is needed.
Dr Moe Elgendi (moe.elgendi@hest.ethz.ch, Biomedical and Mobile Health Technology Research Group, ETH Zurich, http://bmht.hest.ethz.ch) will supervise the student during this project in collaboration with Dr. Jochen Klumpp (jochen.klumpp@hest.ethz.ch, ISG D-HEST, ETH Zurich).
Dr Moe Elgendi (moe.elgendi@hest.ethz.ch, Biomedical and Mobile Health Technology Research Group, ETH Zurich, http://bmht.hest.ethz.ch) will supervise the student during this project in collaboration with Dr. Jochen Klumpp (jochen.klumpp@hest.ethz.ch, ISG D-HEST, ETH Zurich).