Engineering Distributed Graph Algorithms in PGAS languages

1. Engineering Distributed Graph Algorithms in PGAS languages Guojing Cong, IBM research Joint work with George Almasi and Vijay Saraswat

2. Programming language from the perspective of a not-so-distant admirer

3. Mapping graph algorithms onto distributed memory machines has been a challenge • Efficient mapping PRAM algorithm onto SMPs is hard • Mapping onto a cluster of SMPs is even harder • Optimizations are available and shown to improve performance • Can these be somehow automated with help from the language design, compiler and runtime development? • Expectations of the languages • Expressiveness • SPMD, task parallelism (spawn/async), pipeline, future, virtual shared-memory abstraction, work-stealing, data distribution, … • Ease of programming • Efficiency • Mapping high level constructs to run fast on the target machine • SMP • Multi-core, multi-threaded • MPP • Heterogeneous with accelerators • Leverage for tuning

4. A case study with connected components on a cluster of SMPs with UPC • A connected component of an undirected graph G=(V,E), |V|=n, |E|=m, is a maximal connected subgraph • Connected components algorithm find all such components in G • Sequential algorithms • Breadth-first traversal (BFS) • Depth-first traversal (DFS) • One parallel algorithm -- Shiloach-Vishkin algorithm (SV82) • Edge list as input • Adopts the graft and shortcut approach • Start with n isolated vertices. • Graft vertex v to a neighbor u with (u < v) • Shortcut the connected components into super-vertices and continue on the reduced graph

5. Example: SV 4 2 4 2 1,4 2,3 1st iter. 1 1 3 3 Input graph graft shortcut 1 2 1 2 2nd iter.

6. Simple? Yes, performs poorly Sun enterprise E4500 • Memory-intensive, irregular accesses, poor temporal locality

7. Typical behavior of graph algorithms • CPI construction • LRU stack distance plot • BC – betweeness centrality • BiCC – Biconnected components • MST – Minimum spanning tree

8. On distributed-memory machines • Random access and indirection make it hard to • implement, e.g, no fast MPI implementation • Optimize, i.e., random access creates problems for both communication and cache performance • The partitioned global address space (PGAS) paradigm • presents a shared-memory abstraction to the programmer for distributed-memory machines. receives a fair amount of attention recently. • allows the programmer to control the data layout and work assignment • improve ease of programming, and also give the programmer leverage to tune for high performance

9. Implementation in UPC is straightforward UPC implementation Pthread implementation

10. Performance is miserable

11. Communication efficient algorithms • Proposed to address the “bottleneck of processor-to-processor communication” • Goodrich [96] presented a communication-efficient sorting algorithmon weak-CREWBSP that runs in O(log n/ log(h + 1)) communication rounds and O((n log n)/p) local computation time, for h = Θ(n/p) • Adler et. al. [98] presented a communication-optimal MST algorithm • Dehne et al. [02] designed an efficient list ranking algorithm for coarse-grained multicomputers (CGM) and BSP that takes O(log p) communication rounds with O(n/p) local computation • Common approach • simulating several (e.g., O(log p) or O(log log p) ) steps of the PRAM algorithms to reduce the input size so that it fits in the memory of a single node • A “sequential” algorithm is then invoked to process the reduced input of size O(n/p) • finally the result is broadcast to all processors for computing the final solution • Question • How well do communication efficient algorithms work on practice? • How fast can optimized shared-memory based algorithms run? Cache performace vs. communication performance • Can these optimizations be automated through necessary language/compiler support

12. Locality-central optimization • Improve locality behavior of the algorithm • The key performance issues are communication and cache performance • Determined by locality • Many prior cache-friendly results, but no tangible practical evidence • Fine-grain parallelism makes it hard to optimize for temporal locality • Focus on spatial locality • To take advantage of large cache lines, hardware prefetching, software prefetching

13. Scheduling of the memory accesses in a parallel loop Typical loop in CC Generic loop

14. An example

15. Mapping to the distributed environments • All remote accesses are consecutive in our scheduling • If the runtime provides remote prefetching or coalescing, then communication efficiency can be improved • If not, coalescing can be easily done at the program level as shown on right

16. Performance improvement due to communication efficency

17. Applying the approach to single-node for cache-friendly design • Apply as many levels of recursions as necessary • Simulate the recursions with virtual threads • Assuming a large-enough, one level, fully associative cache Original execution time Optimized execution time

18. Graph-specific optimization • Compact edge list • the size of the list determines the number of elements to request from remote nodes • edges within components no longer contribute to the merging of connected components, and can be filtered out • Avoid communication hotspot • Grafting in CC shoots a pointer from a vertex with larger numbering to one with smaller numbering. • Thread thr0 owns vertex 0, and may quickly become a communication hotspot • Avoid querying thr0 about D[0]

19. UPC specific optimization • Avoid runtime cost on local data • After optimization, all direct access to the shared arrays are local • Yet the compiler is not able to recognize • With UPC, we use private pointer arithmetics for • Avoid intrinsics • It is costly to invoke compiler intrinsics to determine the target thread id • Computing target thread ids is done for every iteration. • we compute these ids directly instead of invoking the intrinsics. • Noticing that the target ids do not change across iteration, we compute them once and store them in a global buffer.

20. Performance Results

21. So, how helpful is UPC • Straightforward mapping of shared-memory algorithm is easy • quick prototyping • Quick profiling • Incremental optimization (10 versions for CC) • All other optimizations are manual • Many of them can be automated, though • UPC is not flexible enough to expose the hierarchy of nodes and processors to the programmer

22. Conclusion and future work • We show that with appropriate optimizations, shared-memory graph algorithms can be mapped to the PGAS environment with high performance. • On inputs that fit in the main memory on one node, our implementation achieves good speedups over the best SMP implementation and the best sequential implementation. • Our results suggest that effective use of processors and caches can bring better performance than simply reducing the communication rounds • Automating these optimizations is our future work