Remote OpenMP offloading
OpenMP has a long and successful history in parallel programming for CPUs, and more recently GPUs through accelerator offloading.
1,184 papers · page 12 of 60
Multi-queues can be state-of-the-art priority schedulers
Designing and implementing efficient parallel priority schedulers is an active research area. An intriguing proposed design is the Multi-Queue: given n threads and m ≥ n distinct priority queues, task insertions are performed uniformly at random, while, to delete, a thread picks …
Understanding and detecting deep memory persistency bugs in NVM programs with DeepMC
To facilitate programming with non-volatile memory (NVM), a set of memory persistency models, such as strict and epoch persistency, have been proposed. Although these models provide high-level guidance for reasoning about the data persistence, implementing them correctly is nontr…
Mashup: making serverless computing useful for HPC workflows via hybrid execution
This work introduces Mashup, a novel strategy to leverage serverless computing model for executing scientific workflows in a hybrid fashion by taking advantage of both the traditional VM-based cloud computing platform and the emerging serverless platform. Mashup outperforms the s…
Rethinking graph data placement for graph neural network training on multiple GPUs
The existing Graph Neural Network (GNN) systems adopt graph partitioning to divide the graph data for multi-GPU training. Although they support large graphs, we find that the existing techniques lead to large data loading overhead. In this work, we for the first time model the da…
Elimination (a, b)-trees with fast, durable updates
Many concurrent dictionary implementations are designed and optimized for read-mostly workloads with uniformly distributed keys, and often perform poorly on update-heavy workloads. In this work, we first present a concurrent (a,b)-tree, the OCC-ABtree, which outperforms its faste…
QGTC: accelerating quantized graph neural networks via GPU tensor core
Over the most recent years, quantized graph neural network (QGNN) attracts lots of research and industry attention due to its high robustness and low computation and memory overhead. Unfortunately, the performance gains of QGNN have never been realized on modern GPU platforms. To…
ParGeo: a library for parallel computational geometry
We present ParGeo, a multicore library for computational geometry algorithms. We describe two of the algorithms from ParGeo, convex hull and the smallest enclosing ball, and present a short evaluation of all implementations currently in ParGeo.
FliT: a library for simple and efficient persistent algorithms
Non-volatile random access memory (NVRAM) offers byte-addressable persistence at speeds comparable to DRAM. However, with caches remaining volatile, automatic cache evictions can reorder updates to memory, potentially leaving persistent memory in an inconsistent state upon a syst…
Parallel block-delayed sequences
Programming languages using functions on collections of values, such as map, reduce, scan and filter, have been used for over fifty years. Such collections have proven to be particularly useful in the context of parallelism because such functions are naturally parallel. However, …
A W-cycle algorithm for efficient batched SVD on GPUs
As a fundamental factorization operation, the singular value decomposition (SVD) plays a paramount role in abroad range of domains such as scientific computing and machine learning. Due to its computational bottleneck of factorization for small matrices in real-world applications…
LB-HM: load balance-aware data placement on heterogeneous memory for task-parallel HPC applications
The emergence of heterogeneous memory (HM) provides a cost-effective and high-performance solution to memory-consuming HPC applications. However, using HM, wisely migrating data objects on it is critical for high performance. In this work, we introduce a load balance-aware page m…
An LLVM-based open-source compiler for NVIDIA GPUs
We present GASS, an LLVM-based open-source compiler for NVIDIA GPU's SASS machine assembly. GASS is the first open-source compiler targeting SASS, and it provides a unified toolchain for currently fragmented low-level performance research on NVIDIA GPUs. GASS supports all recent …
High performance GPU concurrent B+tree
Concurrent B+trees have been widely used in many systems from file systems to databases. With the volume of data requests expanding exponentially, the systems are facing tremendous performance pressure. GPUs have shown their potential to accelerate the concurrent B+trees operatio…
Vapro: performance variance detection and diagnosis for production-run parallel applications
Performance variance is a serious problem for parallel applications, which can cause performance degradation and make applications' behavior hard to understand. Therefore, detecting and diagnosing performance variance are of crucial importance for users and application developers…
An ownership policy and deadlock detector for promises
Task-parallel programs often enjoy deadlock freedom under certain restrictions, such as the use of structured join operations, as in Cilk and X10, or the use of asynchronous task futures together with deadlock-avoiding policies such as Known Joins or Transitive Joins. However, th…
Simplifying low-level GPU programming with GAS
Many low-level optimizations for NVIDIA GPU can only be implemented in native hardware assembly (SASS). However, programming in SASS is unproductive and not portable. To simplify low-level GPU programming, we present GAS (Gpu ASsembly), a PTX-like language that provides a stable…
GPTune: multitask learning for autotuning exascale applications
Multitask learning has proven to be useful in the field of machine learning when additional knowledge is available to help a prediction task. We adapt this paradigm to develop autotuning frameworks, where the objective is to find the optimal performance parameters of an applicati…
Advanced synchronization techniques for task-based runtime systems
Task-based programming models like OmpSs-2 and OpenMP provide a flexible data-flow execution model to exploit dynamic, irregular and nested parallelism. Providing an efficient implementation that scales well with small granularity tasks remains a challenge, and bottlenecks can ma…
Scaling implicit parallelism via dynamic control replication
We present dynamic control replication, a run-time program analysis that enables scalable execution of implicitly parallel programs on large machines through a distributed and efficient dynamic dependence analysis. Dynamic control replication distributes dependence analysis by ex…