In-cache query co-processing on coupled CPU-GPU architectures

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1. What have the authors contributed in "In-cache query co-processing on coupled cpu-gpu architectures" ?

2. What future works have the authors mentioned in the paper "In-cache query co-processing on coupled cpu-gpu architectures" ?

3. What is the way to optimize the performance of a query?

4. How many memory stalls are suffered by the CPU CU working on prefetching?

Accepted Answer

In this paper, the authors propose a novel in-cache query co-processing paradigm for main memory On-Line Analytical Processing ( OLAP ) databases on coupled CPU-GPU architectures.. Specifically, the authors adapt CPU-assisted prefetching to minimize cache misses in GPU query co-processing and CPU-assisted decompression to improve query execution performance.. Furthermore, the authors develop a cost model guided adaptation mechanism for distributing the workload of prefetching, decompression, and query execution between CPU and GPU.

Accepted Answer

Additionally, the authors have proposed a cost model to predict the execution time and choose the optimal core assignment plan.. As for future work, the authors are interested in extending their system to row stores ( e. g., by revisiting prefetching and data compres- sion in the context of row stores ) and in exploring the issues discussed in Section 5.. Such improvements show that in-cache query co-processing is promising on coupled CPU-GPU architectures.

Accepted Answer

Query co-processing on discrete GPUs: Because discrete GPUs have much higher bandwidth and massive thread parallelism from CPUs, they are ideal choice for query processing.

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With prefetching enabled, the memory stalls suffered by the CPU CU working on prefetching (i.e. C0) are 73% of the memory unit cycles.

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The inherent GPU design of Single Program Multiple Data (SPMD) execution model and the in-order nature of GPU cores make the GPU in the coupled architecture more sensitive to memory stalls.

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it can significantly reduce the memory stalls so that the efficiency of both the CPU and the GPU can be highly improved.

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As the experimental results show, the in-cache co-processing paradigm can effectively reduce the impact of memory stalls, thus improving the overall performance of TPC-H queries by up to 36% and 40% over the state-of-theart fine-grained method on AMD A8 and A10, respectively.

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as Figures 14 and 15 show, though prefetching has significantly mitigated the cache misses suffered by the GPU, higher bandwidth and larger cache size on future architectures can further increase the efficiency of their query co-processing paradigm.

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due to the unique architectural design of coupled CPU-GPU architectures, such homogeneous workload distribution schemes can hinder query co-processing performance on the GPU.

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The GPU in the coupled architecture has a much smaller number of cores at lower clock frequency because of chip area constraints.

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The performance improvement of PE over the latest co-processing method E [19] is 24% and 22% on the selection and the hash join, respectively.

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The authors abstract three common functional modules in the in-cache query processing paradigm: prefetching (P ), decompression (D, optional), and the actual query execution (E).

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To validate the effectiveness of their cost model, the authors compare the elapsed time and CU assignment plans from measurements with those from the cost model.

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Since the focus of this study is to further improve the performance of query co-processing on coupled CPU-GPU architectures, the authors have omitted the detailed results on discrete architectures.

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As the table shows, when the data compression ratio is better and no decompression is needed, then the selection time can be reduced by 90%.

Accepted Answer

Figure 2c shows the performance of a table scan with random accesses on the GPU when the number of work items that is delayed due to L2 cache miss increases from 0 to 64.

In-cache query co-processing on coupled CPU-GPU architectures

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Most frequently asked questions

1. What have the authors contributed in "In-cache query co-processing on coupled cpu-gpu architectures" ?

2. What future works have the authors mentioned in the paper "In-cache query co-processing on coupled cpu-gpu architectures" ?

3. What is the way to optimize the performance of a query?

4. How many memory stalls are suffered by the CPU CU working on prefetching?

5. What makes the GPU in the coupled architecture more sensitive to memory stalls?

6. What is the effect of prefetching on the performance of the CPU and GPU?

7. How does the in-cache coprocessing paradigm work?

8. What is the effect of prefetching on the performance of the query?

9. What is the main reason why the GPU is not a good choice for query coprocessing?

10. Why is the GPU in the coupled architecture so small?

11. How much performance improvement is PE over the latest co-processing method?

12. What are the three common functional modules in the in-cache query processing paradigm?

13. How do the authors validate the effectiveness of their cost model?

14. Why did the authors omit the detailed results on discrete architectures?

15. How can the data compression ratio be reduced?

16. What is the performance of a table scan with random accesses on the GPU?

Figures

Citations

SONG: Approximate Nearest Neighbor Search on GPU

A performance analysis framework for optimizing OpenCL applications on FPGAs

Understanding Co-Running Behaviors on Integrated CPU/GPU Architectures

Distributed subgraph matching on timely dataflow

Pump Up the Volume: Processing Large Data on GPUs with Fast Interconnects

References

Access path selection in a relational database management system

The OpenCL specification

Integrating compression and execution in column-oriented database systems

A Comprehensive Performance Comparison of CUDA and OpenCL

Relational query coprocessing on graphics processors

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