544 research outputs found
Multi-GPU Graph Analytics
We present a single-node, multi-GPU programmable graph processing library
that allows programmers to easily extend single-GPU graph algorithms to achieve
scalable performance on large graphs with billions of edges. Directly using the
single-GPU implementations, our design only requires programmers to specify a
few algorithm-dependent concerns, hiding most multi-GPU related implementation
details. We analyze the theoretical and practical limits to scalability in the
context of varying graph primitives and datasets. We describe several
optimizations, such as direction optimizing traversal, and a just-enough memory
allocation scheme, for better performance and smaller memory consumption.
Compared to previous work, we achieve best-of-class performance across
operations and datasets, including excellent strong and weak scalability on
most primitives as we increase the number of GPUs in the system.Comment: 12 pages. Final version submitted to IPDPS 201
Lazy Evaluation and Delimited Control
The call-by-need lambda calculus provides an equational framework for
reasoning syntactically about lazy evaluation. This paper examines its
operational characteristics. By a series of reasoning steps, we systematically
unpack the standard-order reduction relation of the calculus and discover a
novel abstract machine definition which, like the calculus, goes "under
lambdas." We prove that machine evaluation is equivalent to standard-order
evaluation. Unlike traditional abstract machines, delimited control plays a
significant role in the machine's behavior. In particular, the machine replaces
the manipulation of a heap using store-based effects with disciplined
management of the evaluation stack using control-based effects. In short, state
is replaced with control. To further articulate this observation, we present a
simulation of call-by-need in a call-by-value language using delimited control
operations
Instruction fetch architectures and code layout optimizations
The design of higher performance processors has been following two major trends: increasing the pipeline depth to allow faster clock rates, and widening the pipeline to allow parallel execution of more instructions. Designing a higher performance processor implies balancing all the pipeline stages to ensure that overall performance is not dominated by any of them. This means that a faster execution engine also requires a faster fetch engine, to ensure that it is possible to read and decode enough instructions to keep the pipeline full and the functional units busy. This paper explores the challenges faced by the instruction fetch stage for a variety of processor designs, from early pipelined processors, to the more aggressive wide issue superscalars. We describe the different fetch engines proposed in the literature, the performance issues involved, and some of the proposed improvements. We also show how compiler techniques that optimize the layout of the code in memory can be used to improve the fetch performance of the different engines described Overall, we show how instruction fetch has evolved from fetching one instruction every few cycles, to fetching one instruction per cycle, to fetching a full basic block per cycle, to several basic blocks per cycle: the evolution of the mechanism surrounding the instruction cache, and the different compiler optimizations used to better employ these mechanisms.Peer ReviewedPostprint (published version
Modulo scheduling with reduced register pressure
Software pipelining is a scheduling technique that is used by some product compilers in order to expose more instruction level parallelism out of innermost loops. Module scheduling refers to a class of algorithms for software pipelining. Most previous research on module scheduling has focused on reducing the number of cycles between the initiation of consecutive iterations (which is termed II) but has not considered the effect of the register pressure of the produced schedules. The register pressure increases as the instruction level parallelism increases. When the register requirements of a schedule are higher than the available number of registers, the loop must be rescheduled perhaps with a higher II. Therefore, the register pressure has an important impact on the performance of a schedule. This paper presents a novel heuristic module scheduling strategy that tries to generate schedules with the lowest II, and, from all the possible schedules with such II, it tries to select that with the lowest register requirements. The proposed method has been implemented in an experimental compiler and has been tested for the Perfect Club benchmarks. The results show that the proposed method achieves an optimal II for at least 97.5 percent of the loops and its compilation time is comparable to a conventional top-down approach, whereas the register requirements are lower. In addition, the proposed method is compared with some other existing methods. The results indicate that the proposed method performs better than other heuristic methods and almost as well as linear programming methods, which obtain optimal solutions but are impractical for product compilers because their computing cost grows exponentially with the number of operations in the loop body.Peer ReviewedPostprint (published version
Calculating with lenses: optimising bidirectional transformations
This paper presents an equational calculus to reason about bidirectional transformations specified in the point-free style. In particular, it focuses on the so-called lenses as a bidirectional idiom, and shows that many standard laws characterising point-free combinators and recursion patterns are also valid in that setting. A key result is that uniqueness also holds for bidirectional folds and unfolds, thus unleashing the power of fusion as a program optimisation technique. A rewriting system for automatic lens optimisation is also presented, to prove the usefulness of the proposed calculus.(undefined
Gunrock: GPU Graph Analytics
For large-scale graph analytics on the GPU, the irregularity of data access
and control flow, and the complexity of programming GPUs, have presented two
significant challenges to developing a programmable high-performance graph
library. "Gunrock", our graph-processing system designed specifically for the
GPU, uses a high-level, bulk-synchronous, data-centric abstraction focused on
operations on a vertex or edge frontier. Gunrock achieves a balance between
performance and expressiveness by coupling high performance GPU computing
primitives and optimization strategies with a high-level programming model that
allows programmers to quickly develop new graph primitives with small code size
and minimal GPU programming knowledge. We characterize the performance of
various optimization strategies and evaluate Gunrock's overall performance on
different GPU architectures on a wide range of graph primitives that span from
traversal-based algorithms and ranking algorithms, to triangle counting and
bipartite-graph-based algorithms. The results show that on a single GPU,
Gunrock has on average at least an order of magnitude speedup over Boost and
PowerGraph, comparable performance to the fastest GPU hardwired primitives and
CPU shared-memory graph libraries such as Ligra and Galois, and better
performance than any other GPU high-level graph library.Comment: 52 pages, invited paper to ACM Transactions on Parallel Computing
(TOPC), an extended version of PPoPP'16 paper "Gunrock: A High-Performance
Graph Processing Library on the GPU
Decrypting The Java Gene Pool: Predicting Objects' Lifetimes with Micro-patterns
Pretenuring long-lived and immortal objects into infrequently or never collected regions reduces garbage collection costs significantly. However, extant approaches either require computationally expensive, application-specific, off-line profiling, or consider only allocation sites common to all programs, i.e. invoked by the virtual machine rather than application programs. In contrast, we show how a simple program analysis, combined with an object lifetime knowledge bank, can be exploited to match both runtime system and application program structure with object lifetimes. The complexity of the analysis is linear in the size of the program, so need not be run ahead of time. We obtain performance gains between 6-77% in GC time against a generational copying collector for several SPEC jvm98 programs
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