skip to main content

Title: Scalable All-pairs Shortest Paths for Huge Graphs on Multi-GPU Clusters
We present an optimized Floyd-Warshall (Floyd-Warshall) algorithm that computes the All-pairs shortest path (APSP) for GPU accelerated clusters. The Floyd-Warshall algorithm due to its structural similarities to matrix-multiplication is well suited for highly parallel GPU architectures. To achieve high parallel efficiency, we address two key algorithmic challenges: reducing high communication overhead and addressing limited GPU memory. To reduce high communication costs, we redesign the parallel (a) to expose more parallelism, (b) aggressively overlap communication and computation with pipelined and asynchronous scheduling of operations, and (c) tailored MPI-collective. To cope with limited GPU memory, we employ an offload model, where the data resides on the host and is transferred to GPU on-demand. The proposed optimizations are supported with detailed performance models for tuning. Our optimized parallel Floyd-Warshall implementation is up to 5x faster than a strong baseline and achieves 8.1 PetaFLOPS/sec on 256~nodes of the Summit supercomputer at Oak Ridge National Laboratory. This performance represents 70% of the theoretical peak and 80% parallel efficiency. The offload algorithm can handle 2.5x larger graphs with a 20% increase in overall running time.  more » « less
Award ID(s):
Author(s) / Creator(s):
; ; ; ; ;
Date Published:
Journal Name:
HPDC '21: Proceedings of the 30th International Symposium on High-Performance Parallel and Distributed Computing
Page Range / eLocation ID:
121 to 131
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Alternating Least Square (ALS) is a classic algorithm to solve matrix factorization widely used in recommendation systems. Existing efforts focus on parallelizing ALS on multi-/many-core platforms to handle large datasets. Recently, an optimized ALS variant called eALS was proposed, and it yields significantly lower time complexity and higher recommending accuracy than ALS. However, it is challenging to parallelize eALS on modern parallel architectures (e.g., CPUs and GPUs) because: 1) eALS’ data dependence prevents it from fine-grained parallel execution, thus eALS cannot fully utilize GPU's massive parallelism, 2) the sparsity of input data causes poor data locality and unbalanced workload, and 3) its large memory usage cannot fit into GPU's limited on-device memory, particularly for real-world large datasets. This paper proposes an efficient CPU/GPU heterogeneous recommendation system based on fast eALS for the first time (called HEALS) that consists of a set of system optimizations. HEALS employs newly designed architecture-adaptive data formats to achieve load balance and good data locality on CPU and GPU. HEALS also presents a CPU/GPU collaboration model that can explore both task parallelism and data parallelism. HEALS also optimizes this collaboration model with data communication overlapping and dynamic workload partition between CPU and GPU. Moreover, HEALS is further enhanced by various parallel techniques (e.g., loop unrolling, vectorization, and GPU parallel reduction). Evaluation results show that HEALS outperforms other state-of-the-art baselines in both performance and recommendation quality. Particularly, HEALS achieves up to 5.75 x better performance than a state-of-the-art ALS GPU library. This work also demonstrates the possibility of conducting fast recommendations on large datasets with constrained (or relaxed) hardware resources, e.g, a single CPU/GPU node. 
    more » « less
  2. null (Ed.)
    We are motivated by newly proposed methods for data mining large-scale corpora of scholarly publications, such as the full biomedical literature, which may consist of tens of millions of papers spanning decades of research. In this setting, analysts seek to discover how concepts relate to one another. They construct graph representations from annotated text databases and then formulate the relationship-mining problem as one of computing all-pairs shortest paths (APSP), which becomes a significant bottleneck. In this context, we present a new high-performance algorithm and implementation of the Floyd-Warshall algorithm for distributed-memory parallel computers accelerated by GPUs, which we call DSNAPSHOT (Distributed Accelerated Semiring All-Pairs Shortest Path). For our largest experiments, we ran DSNAPSHOT on a connected input graph with millions of vertices using 4, 096nodes (24,576GPUs) of the Oak Ridge National Laboratory's Summit supercomputer system. We find DSNAPSHOT achieves a sustained performance of 136×1015 floating-point operations per second (136petaflop/s) at a parallel efficiency of 90% under weak scaling and, in absolute speed, 70% of the best possible performance given our computation (in the single-precision tropical semiring or “min-plus” algebra). Looking forward, we believe this novel capability will enable the mining of scholarly knowledge corpora when embedded and integrated into artificial intelligence-driven natural language processing workflows at scale. 
    more » « less
  3. To deliver scalable performance to large-scale scientific and data analytic applications, HPC cluster architectures adopt the distributed-memory model. The performance and scalability of parallel applications on such systems are limited by the communication cost across compute nodes. Therefore, projecting the minimum communication cost and maximum scalability of the user applications plays a critical role in assessing the benefits of porting these applications to HPC clusters as well as developing efficient distributed-memory implementations. Unfortunately, this task is extremely challenging for end users, as it requires comprehensive knowledge of the target application and hardware architecture and demands significant effort and time for manual system analysis. To streamline the process of porting user applications to HPC clusters, this paper presents CommAnalyzer, an automated framework for estimating the communication cost on distributed-memory models from sequential code. CommAnalyzer uses novel dynamic program analyses and graph algorithms to capture the inherent flow of program values (information) in sequential code to estimate the communication when this code is ported to HPC clusters. Therefore, CommAnalyzer makes it possible to project the efficiency/scalability upper-bound (i.e., Roofline) of the effective distributed-memory implementation before even developing one. The experiments with real-world, regular and irregular HPC applications demonstrate the utility of CommAnalyzer in estimating the minimum communication of sequential applications on HPC clusters. In addition, the optimized MPI+X implementations achieve more than 92% of the efficiency upper-bound across the different workloads. 
    more » « less
  4. To alleviate bottlenecks in storing and accessing data on high-performance computing (HPC) systems, I/O libraries are enabling computation while data is in-transit, such as HDFS filters. For scientific applications that commonly use floating-point data, error-bounded lossy compression methods are a critical technique to significantly reduce the storage and bandwidth requirements. Thus far, deciding when and where to schedule in-transit data transformations, such as compression, has been outside the scope of I/O libraries. In this paper, we introduce Runway, a runtime framework that enables computation on in-transit data with an object storage abstraction. Runway is designed to be extensible to execute user-defined functions at runtime. In this effort, we focus on studying methods to offload data compression operations to available processing units based on latency and throughput. We compare the performance of running compression on multi-core CPUs, as well as offloading it to a GPU and a Data Processing Unit (DPU). We implement a state-of-the-art error-bounded lossy compression algorithm, SZ3, as a Runway function with a variant optimized for DPUs. We propose dynamic modeling to guide scheduling decisions for in-transit data compression. We evaluate Runway using four scientific datasets from the SDRBench benchmark suite on a the Perlmutter supercomputer at NERSC. 
    more » « less
  5. We present BurstZ, a bandwidth-efficient accelerator platform for scientific computing. While accelerators such as GPUs and FPGAs provide enormous computing capabilities, their effectiveness quickly deteriorates once the working set becomes larger than the on-board memory capacity, causing the performance to become bottlenecked either by the communication bandwidth between the host and the accelerator. Compression has not been very useful in solving this issue due to the difficulty of efficiently compressing floating point numbers, which scientific data often consists of. Most compression algorithms are either ineffective with floating point numbers, or has a high performance overhead. BurstZ is an FPGA-based accelerator platform which addresses the bandwidth issue via a novel hardware-optimized floating point compression algorithm, which we call sZFP. We demonstrate that BurstZ can completely remove the communication bottleneck for accelerators, using a 3D stencil-code accelerator implemented on a prototype BurstZ implementation. Evaluated against hand-optimized implementations of stencil code accelerators of the same architecture, our BurstZ prototype outperformed an accelerator without compression by almost 4X, and even an accelerator with enough memory for the entire dataset by over 2X. BurstZ improved communication efficiency so much, our prototype was even able to outperform the upper limit projected performance of an optimized stencil core with ideal memory access characteristics, by over 2X. 
    more » « less