26,098 papers · page 436 of 1,305
In this paper, we propose an in-memory computing framework (called GPF) that provides a set of genomic formats, APIs and a fast genomic engine for large-scale genomic data processing. Our GPF comprises two main components: (1) scalable genomic data formats and API. (2) an advance…
Massive spatial parallelism at low energy gives FPGAs the potential to be core components in large scale high performance computing (HPC) systems. In this paper we present four major design steps that harness high-level synthesis (HLS) to implement scalable spatial FPGA algorithm…
General sparse matrix-matrix multiplication (SpGEMM) is an essential building block in a number of applications. In our work, we fully utilize GPU registers and shared memory to implement an efficient and load balanced SpGEMM in comparison with the existing implementations.
Growing accuracy and robustness of Deep Neural Networks (DNN) models are accompanied by growing model capacity (going deeper or wider). However, high memory requirements of those models make it difficult to execute the training process in one GPU. To address it, we first identify…
Aggressive technology scaling trends have made the hardware of high performance computing (HPC) systems more susceptible to faults. Some of these faults can lead to silent data corruption (SDC), and represent a serious problem because they alter the HPC simulation results. In thi…
Simplified parallel programming coupled with an ability to express speculative computation is realized with Software Transactional Memory (STM). Although STMs are gaining popularity because of significant improvements in parallel performance, they exhibit enormous variation in tr…
Modern DNN frameworks exploit GPU acceleration by default to achieve high performance. The limitation of GPU memory capacity becomes a serious problem because DNNs are becoming deeper and larger. This paper proposes a purely software-based transparent solution, called tvDNN, to t…
The memory reclamation problem is to determine, for any given allocated memory node, when there are no more references to the node, allowing it to be safely returned to the memory management system. In a concurrent context, the memory reclamation problem is highly non-trivial, si…
Concurrent non-blocking hash tries have good cache locality, and horizontally scalable operations. However, operations on most existing concurrent hash tries run in O(log n) time.
The recent advent of compute-intensive GPU architecture has allowed application developers to explore high-order 3D stencils for better computational accuracy. A common optimization strategy for such stencils is to expose sufficient data reuse by means such as loop unrolling, wit…
Interaction with physical objects often imposes latency requirements to multi-core embedded systems. One consequence is the need for synchronisation algorithms that provide predictable latency, in addition to high throughput. We present a synchronisation algorithm that needs at m…
Accelerators can offer exceptional performance advantages. However, programmers need to spend considerable efforts on acceleration, without knowing how sustainable the employed programming models, languages and tools are. To tackle this challenge, we propose and demonstrate a new…
Ordered (key-value) maps are an important and widely-used data type for large-scale data processing frameworks. Beyond simple search, insertion and deletion, more advanced operations such as range extraction, filtering, and bulk updates form a critical part of these frameworks.
Performance variance becomes increasingly challenging on current large-scale HPC systems. Even using a fixed number of computing nodes, the execution time of several runs can vary significantly. Many parallel programs executing on supercomputers suffer from such variance. Perform…
Basic microarchitectural features of NVIDIA GPUs have been stable for a decade, and many analytic solutions were proposed to model their performance. We present a way to review, systematize, and evaluate these approaches by using a microbenchmark. In this manner, we produce a bri…
Sparse triangular solve (SpTRSV) is one of the most important kernels in many real-world applications. Currently, much research on parallel SpTRSV focuses on level-set construction for reducing the number of inter-level synchronizations. However, the out-of-control data reuse and…
Going deeper and wider in neural architectures improves their accuracy, while the limited GPU DRAM places an undesired restriction on the network design domain. Deep Learning (DL) practitioners either need to change to less desired network architectures, or nontrivially dissect a…
Replicas 1 of a vertex play an important role in existing distributed graph processing systems which make a single vertex to be parallel processed by multiple machines and access remote neighbors locally without any remote access. However, replicas of vertices introduce data cohe…
In this paper we present interval-based reclamation (IBR), a new approach to safe reclamation of disconnected memory blocks in nonblocking concurrent data structures. Safe reclamation is a difficult problem: a thread, before freeing a block, must ensure that no other threads are …
Dynamic data race detectors are valuable tools for testing and validating concurrent software, but to achieve good performance they are typically implemented using sophisticated concurrent algorithms. Thus, they are ironically prone to the exact same kind of concurrency bugs they…