The discrete Fourier transform (DFT) and its specialized case, the number theoretic transform (NTT), are two important mathematical tools having applications in several areas of science and engineering. However, despite their usefulness and utility, their adoption continues to be a challenge as computing the DFT of a signal can be a time-consuming and expensive operation. To speed things up, fast Fourier transform (FFT) algorithms, which are reduced-complexity formulations for computing the DFT of a sequence, have been proposed and implemented for traditional processors and their corresponding instruction sets. With the rise of GPUs, NVIDIA introduced its own FFT computation library called cuFFT, which leverages the power of GPUs to compute the DFT. However, as this paper demonstrates, there is a lot of room for improvement to accelerate the FFT and NTT algorithms on modern GPUs by utilizing specialized operations and architectural advancements. In particular, we present four major types of optimizations that leverage tensor cores and the warp-shuffle instruction. Through extensive evaluations, we show that our approach consistently outperforms existing GPU-based implementations with a speedup of up to 4× for NTT and a speed of up to 1.5× for FFT.

The fast Fourier Transform (FFT), a reduced-complexity formulation of the Discrete Fourier Transform (DFT), is an important tool in many areas of science and engineering. FFTW is a well-known package that follows this approach and is currently one of the fastest available implementations of the FFT. NVIDIA introduced its version of FFTW called cuFFT that achieves high performance on the GPUs. In this work we present a novel way to map the FFT algorithm on the newly introduced Tensor Cores by adapting the the Cooley-Tukey recursive FFT algorithm. We present four major types of optimizations that enhance the performance of our approach for varying FFT sizes and show that the approach consistently outperforms cuFFT with a speedup of about 15% to 250% on average.

The current Deep Learning (DL) landscape is fast-paced and is rife with non-uniform models, hardware/software (HW/SW) stacks. Currently, there is no DL benchmarking platform to facilitate the evaluation and comparison of DL innovations, be it models, frameworks, libraries, or hardware. As a result, the current practice of evaluating the benefits of proposed DL innovations is both arduous and error-prone - stifling the adoption of the innovations. In this work, we first identify 10 design features that are desirable within a DL benchmarking platform. These features include: performing the evaluation in a consistent, reproducible, and scalable manner, being framework and hardware agnostic, supporting real-world benchmarking workloads, providing in-depth model execution inspection across the HW/SW stack levels, etc. We then propose MLModelScope, a DL benchmarking platform that realizes these 10 design objectives. MLModelScope introduces a specification to define DL model evaluations and provides a runtime to provision the evaluation workflow using the user-specified HW/SW stack. MLModelScope defines abstractions for frameworks and supports the board range of DL models and evaluation scenarios. We implement MLModelScope as an open-source project with support for all major frameworks and hardware architectures. Through MLModelScope's evaluation and automated analysis workflows, we perform a case-study analysis of 37 models across 4 systems and show how model, hardware, and framework selection affects model accuracy and performance under different benchmarking scenarios. We further demonstrate how MLModelScope's tracing capability gives a holistic view of model execution and helps pinpoint bottlenecks.

The popularity of data- and scientific-oriented applications, abundance of on-demand compute resources, and scarcity of domain expert programmers have given rise to high-level scripting languages. These high-level scripting languages offer a fast way to translate ideas into code, but tend to pay a heavy performance overhead. In order to alleviate the performance penalty, each implementation of these languages often offers a compilation path to a subset of the language.

There has been a rapid proliferation of machine learning/deep learning (ML) models and wide adoption of them in many application domains. This has made profiling and characterization of ML model performance an increasingly pressing task for both hardware designers and system providers, as they would like to offer the best possible system to serve ML models with the target latency, throughput, cost, and energy requirements while maximizing resource utilization. Such an endeavor is challenging as the characteristics of an ML model depend on the interplay between the model, framework, system libraries, and the hardware (or the HW/SW stack). Existing profiling tools are disjoint, however, and only focus on profiling within a particular level of the stack, which limits the thoroughness and usefulness of the profiling results.This paper proposes XSP - an across-stack profiling design that gives a holistic and hierarchical view of ML model execution. XSP leverages distributed tracing to aggregate and correlate profile data from different sources. XSP introduces a leveled and iterative measurement approach that accurately captures the latencies at all levels of the HW/SW stack in spite of the profiling overhead. We couple the profiling design with an automated analysis pipeline to systematically analyze 65 state-of-the-art ML models. We demonstrate that XSP provides insights which would be difficult to discern otherwise.

As Deep Learning (DL) models have been increasingly used in latency-sensitive applications, there has been a growing interest in improving their response time. An important venue for such improvement is to profile the execution of these models and characterize their performance to identify possible optimization opportunities. However, the current profiling tools lack the highly desired abilities to characterize ideal performance, identify sources of inefficiency, and quantify the benefits of potential optimizations. Such deficiencies have led to slow characterization/optimization cycles that cannot keep up with the fast pace at which new DL models are introduced.We propose Benanza, a sustainable and extensible benchmarking and analysis design that speeds up the characterization/optimization cycle of DL models on GPUs. Benanza consists of four major components: a model processor that parses models into an internal representation, a configurable benchmark generator that automatically generates micro-benchmarks given a set of models, a database of benchmark results, and an analyzer that computes the "lower-bound" latency of DL models using the benchmark data and informs optimizations of model execution. The "lower-bound" latency metric estimates the ideal model execution on a GPU system and serves as the basis for identifying optimization opportunities in frameworks or system libraries. We used Benanza to evaluate 30 ONNX models in MXNet, ONNX Runtime, and PyTorch on 7 GPUs ranging from Kepler to the latest Turing, and identified optimizations in parallel layer execution, cuDNN convolution algorithm selection, framework inefficiency, layer fusion, and using Tensor Cores.

The past few years have seen a surge of applying Deep Learning (DL) models for a wide array of tasks such as image classification, object detection, machine translation, etc. While DL models provide an opportunity to solve otherwise intractable tasks, their adoption relies on them being optimized to meet target latency and resource requirements. Benchmarking is a key step in this process but has been hampered in part due to the lack of representative and up-to-date benchmarking suites. This paper proposes DLBricks, a composable benchmark generation design that reduces the effort of developing, maintaining, and running DL benchmarks. DLBricks decomposes DL models into a set of unique runnable networks and constructs the original model's performance using the performance of the generated benchmarks. Since benchmarks are generated automatically and the benchmarking time is minimized, DLBricks can keep up-to-date with the latest proposed models, relieving the pressure of selecting representative DL models. We evaluate DLBricks using 50 MXNet models spanning 5 DL tasks on 4 representative CPU systems. We show that DLBricks provides an accurate performance estimate for the DL models and reduces the benchmarking time across systems (e.g. within 95% accuracy and up to 4.4× benchmarking time speedup on Amazon EC2 c5.xlarge).

Deep neural networks (DNNs) have become core computation components within low latency Function as a Service (FaaS) prediction pipelines. Cloud computing, as the defacto backbone of modern computing infrastructure, has to be able to handle user-defined FaaS pipelines containing diverse DNN inference workloads while maintaining isolation and latency guarantees with minimal resource waste. The current solution for guaranteeing isolation and latency within FaaS is inefficient. A major cause of the inefficiency is the need to move large amount of data within and across servers. We propose TrIMS as a novel solution to address this issue. TrIMSis a generic memory sharing technique that enables constant data to be shared across processes or containers while still maintaining isolation between users. TrIMS consists of a persistent model store across the GPU, CPU, local storage, and cloud storage hierarchy, an efficient resource management layer that provides isolation, and a succinct set of abstracts, applicationAPIs, and container technologies for easy and transparent integration with FaaS, Deep Learning (DL) frameworks, and user code. We demonstrate our solution by interfacing TrIMS with the Apache MXNet framework and demonstrate up to 24x speedup in latency for image classification models, up to 210x speedup for large models, and up to8×system throughput improvement.

Driven by deep learning, there has been a surge of specialized processors for matrix multiplication, referred to as Tensor Core Units (TCUs). These TCUs are capable of performing matrix multiplications on small matrices (usually 4 × 4 or 16 × 16) to accelerate HPC and deep learning workloads. Although TCUs are prevalent and promise increase in performance and/or energy efficiency, they suffer from over specialization as only matrix multiplication on small matrices is supported. In this paper we express both reduction and scan in terms of matrix multiplication operations and map them onto TCUs. To our knowledge, this paper is the first to try to broaden the class of algorithms expressible as TCU operations and is the first to show benefits of this mapping in terms of: program simplicity, efficiency, and performance. We implemented the reduction and scan algorithms using NVIDIA's V100 TCUs and achieved 89% -- 98% of peak memory copy bandwidth. Our results are orders of magnitude faster (up to 100 × for reduction and 3 × for scan) than state-of-the-art methods for small segment sizes (common in HPC and deep learning applications). Our implementation achieves this speedup while decreasing the power consumption by up to 22% for reduction and 16% for scan.

The current landscape of cognitive pipelines exercises many Machine Learning (ML) and Deep Learning (DL) building blocks. These ML and DL building blocks leverage non-uniform frameworks, models, and system stacks. Currently, there is no end-to-end tool that facilitates ML and DL building blocks evaluation and introspection within cognitive pipelines. Due to the absence of such tools, the current practice for evaluating and comparing the benefits of hardware or software innovations on end-to-end cognitive pipelines is both arduous and error-prone - stifling the rate of adoption of innovations. We propose MLModelScope: a hardware/software agnostic platform to facilitate evaluation and introspection of cognitive pipelines in the cloud or on the edge. We describe the design and implementation of MLModelScope and show how it provides a holistic view of the execution of components within cognitive pipelines. MLModelScope aids application developers in experimenting with and discovering cognitive models, data scientists in comparing and evaluating published algorithms, and system architects in optimizing system stacks for cognitive applications.

Data-intensive applications such as machine learning and analytics have created a demand for faster interconnects to avert the memory bandwidth wall and allow GPUs to be effectively leveraged for lower compute intensity tasks. This has resulted in wide adoption of heterogeneous systems with varying underlying interconnects, and has delegated the task of understanding and copying data to the system or application developer. No longer is a malloc followed by memcpy the only or dominating modality of data transfer; application developers are faced with additional options such as unified memory and zero-copy memory. Data transfer performance on these systems is now impacted by many factors including data transfer modality, system interconnect hardware details, CPU caching state, CPU power management state, driver policies, virtual memory paging efficiency, and data placement.

We present Tangram, a programming system for writing performance-portable programs. The language enables programmers to write computation and composition codelets, supported by tuning knobs and primitives for expressing data parallelism and work decomposition. The compiler and runtime use a set of techniques such as hierarchical composition, coarsening, data placement, tuning, and runtime selection based on input characteristics and micro-profiling. The resulting performance is competitive with optimized vendor libraries.

Functional algorithmic skeletons promise a high-level programming interface for distributed-memory clusters that free developers from concerns of task decomposition, scheduling, and communication. Unfortunately, prior distributed functional skeleton frameworks do not deliver performance comparable to that achievable in a low-level distributed programming model such as C with MPI and OpenMP, even when used in concert with high-performance array libraries. There are several causes: they do not take advantage of shared memory on each cluster node; they impose a fixed partitioning strategy on input data; and they have limited ability to fuse loops involving skeletons that produce a variable number of outputs per input.