C-DAC's efforts: application kernels on HPC cluster with GPU accelerators
Why this work is in the frame
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Bibliographic record
Abstract
We describe the problem of parallelization of finite difference method (FDM) and finite element method (FEM) computations for certain class of partial differential equations (PDEs) on High Performance Computing (HPC) GPU cluster. For FDM, the structured grids have been employed and optimal data rearrangement operations are performed in GPU computations. For FEM, unstructured triangular and hexahedral meshes are generated and graph partitioning METIS [14] software is used to generate load-balanced sub-domains. The iterative methods have been used to solve result algebraic matrix system of linear equations. A combination of MPI with CUDA and OpenCL enabled NVIDIA as well as OpenCL based AMD-ATI GPUs of HPC GPU Cluster have been used in our experiments [4,6,7,8]. Our experiments indicate that the MPI-CUDA codes based on FDM and FEM achieves nearly 6x speed-ups for large mesh sizes in comparison to host-cpu implementation of the same code. The un-optimized OpenCL implementation GPU times have shown marginal improvement in speed-ups whereas counterpart the CUDA codes achieved maximum speedup of 4x to 6x on HPC GPU Cluster. We presented performance analysis for different mesh sizes that prove performance capabilities of performance and scalability of FDM and FEM computations GPU cluster.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it