More Like this

1. Exploration on Task Scheduling Strategy of CPU-GPU Heterogeneous Computing System

   Juan Fang, Jiaxing Zhang (January 2022, 2020 IEEE Computer Society Annual Symposium on VLSI (ISVLSI))

   For a CPU-GPU heterogeneous computing system, different types of processors have load balancing problems in the calculation process. What’s more, multitasking cannot be matched to the appropriate processor core is also an urgent problem to be solved. In this paper, we propose a task scheduling strategy for high-performance CPU-GPU heterogeneous computing platform to solve these problems. For the single task model, a task scheduling strategy based on loadaware for CPU-GPU heterogeneous computing platform is proposed. This strategy detects the computing power of the CPU and GPU to process specified tasks, and allocates computing tasks to the CPU and GPU according to the perception ratio. The tasks are stored in a bidirectional queue to reduce the additional overhead brought by scheduling. For the multi-task model, a task scheduling strategy based on the genetic algorithm for CPU-GPU heterogeneous computing platform is proposed. The strategy aims at improving the overall operating efficiency of the system, and accurately binds the execution relationship between different types of tasks and heterogeneous processing cores. Our experimental results show that the scheduling strategy can improve the efficiency of parallel computing as well as system performance. 

   more » « less
2. Orchestrating data placement and query execution in heterogeneous CPU-GPU DBMS

   https://doi.org/10.14778/3551793.3551809  

   Yogatama, Bobbi W.; Gong, Weiwei; Yu, Xiangyao (July 2022, Proceedings of the VLDB Endowment)

   There has been a growing interest in using GPU to accelerate data analytics due to its massive parallelism and high memory bandwidth. The main constraint of using GPU for data analytics is the limited capacity of GPU memory. Heterogeneous CPU-GPU query execution is a compelling approach to mitigate the limited GPU memory capacity and PCIe bandwidth. However, the design space of heterogeneous CPU-GPU query execution has not been fully explored. We aim to improve state-of-the-art CPU-GPU data analytics engine by optimizing data placement and heterogeneous query execution. First, we introduce a semantic-aware fine-grained caching policy which takes into account various aspects of the workload such as query semantics, data correlation, and query frequency when determining data placement between CPU and GPU. Second, we introduce a heterogeneous query executor which can fully exploit data in both CPU and GPU and coordinate query execution at a fine granularity. We integrate both solutions in Mordred, our novel hybrid CPU-GPU data analytics engine. Evaluation on the Star Schema Benchmark shows that the semantic-aware caching policy can outperform the best traditional caching policy by up to 3x. Compared to existing GPU DBMSs, Mordred can outperform by an order of magnitude. 

   more » « less
3. Offload Annotations: Bringing Heterogeneous Computing to Existing Libraries and Workloads

   Yuan, Gina; Palkar, Shoumik; Narayanan, Deepak; and Zaharia, Matei. (July 2020, USENIX ATC 2020)

   null (Ed.)

   As specialized hardware accelerators such as GPUs become increasingly popular, developers are looking for ways to target these platforms with high-level APIs. One promising approach is kernel libraries such as PyTorch or cuML, which provide interfaces that mirror CPU-only counterparts such as NumPy or Scikit-Learn. Unfortunately, these libraries are hard to develop and to adopt incrementally: they only support a subset of their CPU equivalents, only work with datasets that fit in device memory, and require developers to reason about data placement and transfers manually. To address these shortcomings, we present a new approach called offload annotations (OAs) that enables heterogeneous GPU computing in existing workloads with few or no code modifications. An annotator annotates the types and functions in a CPU library with equivalent kernel library functions and provides an offloading API to specify how the inputs and outputs of the function can be partitioned into chunks that fit in device memory and transferred between devices. A runtime then maps existing CPU functions to equivalent GPU kernels and schedules execution, data transfers and paging. In data science workloads using CPU libraries such as NumPy and Pandas, OAs enable speedups of up to 1200⇥ and a median speedup of 6.3⇥ by transparently offloading functions to a GPU using existing kernel libraries. In many cases, OAs match the performance of handwritten heterogeneous implementations. Finally, OAs can automatically page data in these workloads to scale to datasets larger than GPU memory, which would need to be done manually with most current GPU libraries. 

   more » « less
4. Fine-grained dynamic load balancing in spatial join by work stealing on distributed memory

   https://doi.org/10.1145/3557915.3560936  

   Yang, Jie; Puri, Satish; Zhou, Hui (November 2022, Proceedings of the 30th International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL))

   Spatial join is an important operation for combining spatial data. Parallelization is essential for improving spatial join performance. However, load imbalance due to data skew limits the scalability of parallel spatial join. There are many work sharing techniques to address this problem in a parallel environment. One of the techniques is to use data and space partitioning and then scheduling the partitions among threads/processes with the goal of minimizing workload differences across threads/processes. However, load imbalance still exists due to differences in join costs of different pairs of input geometries in the partitions. For the load imbalance problem, we have designed a work stealing spatial join system (WSSJ-DM) on a distributed memory environment. Work stealing is an approach for dynamic load balancing in which an idle processor steals computational tasks from other processors. This is the first work that uses work stealing concept (instead of work sharing) to parallelize spatial join computation on a large compute cluster. We have evaluated the scalability of the system on shared and distributed memory. Our experimental evaluation shows that work stealing is an effective strategy. We compared WSSJ-DM with work sharing implementations of spatial join on a high performance computing environment using partitioned and un-partitioned datasets. Static and dynamic load balancing approaches were used for comparison. We study the effect of memory affinity in work stealing operations involved in spatial join on a multi-core processor. WSSJ-DM performed spatial join using ST_Intersection on Lakes (8.4M polygons) and Parks (10M polygons) in 30 seconds using 35 compute nodes on a cluster (1260 CPU cores). A work sharing Master-Worker implementation took 160 seconds in contrast. 

   more » « less
5. Efficient Parallel and Adaptive Partitioning for Load-balancing in Spatial Join

   Yang, Jie; Puri, Satish (May 2020, 34th IEEE International Parallel & Distributed Processing Symposium)

   Due to the developments of topographic techniques, clear satellite imagery, and various means for collecting information, geospatial datasets are growing in volume, complexity, and heterogeneity. For efficient execution of spatial computations and analytics on large spatial data sets, parallel processing is required. To exploit fine-grained parallel processing in large scale compute clusters, partitioning in a load-balanced way is necessary for skewed datasets. In this work, we focus on spatial join operation where the inputs are two layers of geospatial data. Our partitioning method for spatial join uses Adaptive Partitioning (ADP) technique, which is based on Quadtree partitioning. Unlike existing partitioning techniques, ADP partitions the spatial join workload instead of partitioning the individual datasets separately to provide better load-balancing. Based on our experimental evaluation, ADP partitions spatial data in a more balanced way than Quadtree partitioning and Uniform grid partitioning. ADP uses an output-sensitive duplication avoidance technique which minimizes duplication of geometries that are not part of spatial join output. In a distributed memory environment, this technique can reduce data communication and storage requirements compared to traditional methods. To improve the performance of ADP, an MPI+Threads based parallelization is presented. With ParADP, a pair of real world datasets, one with 717 million polylines and another with 10 million polygons, is partitioned into 65,536 grid cells within 7 seconds. ParADP performs well with both good weak scaling up to 4,032 CPU cores and good strong scaling up to 4,032 CPU cores. 

   more » « less