抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments. Tensor-Flow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general-purpose GPUs, and custom-designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexibility to the application developer: whereas in previous "parameter server" designs the management of shared state is built into the system, TensorFlow enables developers to experiment with novel optimizations and training algorithms. TensorFlow supports a variety of applications, with a focus on training and inference on deep neural networks. Several Google services use TensorFlow in production, we have released it as an open-source project, and it has become widely used for machine learning research. In this paper, we describe the TensorFlow dataflow model and demonstrate the compelling performance that TensorFlow achieves for several real-world applications.

/pdf/tensorflow-a-system-for-large-scale-machine-learning-10wgdjhl6k.pdf

TensorFlow: a system for large-scale machine learning

TensorFlow is an interface for expressing machine learning algorithms, and an
implementation for executing such algorithms. A computation expressed using
TensorFlow can be executed with little or no change on a wide variety of
heterogeneous systems, ranging from mobile devices such as phones and tablets
up to large-scale distributed systems of hundreds of machines and thousands of
computational devices such as GPU cards. The system is flexible and can be used
to express a wide variety of algorithms, including training and inference
algorithms for deep neural network models, and it has been used for conducting
research and for deploying machine learning systems into production across more
than a dozen areas of computer science and other fields, including speech
recognition, computer vision, robotics, information retrieval, natural language
processing, geographic information extraction, and computational drug
discovery. This paper describes the TensorFlow interface and an implementation
of that interface that we have built at Google. The TensorFlow API and a
reference implementation were released as an open-source package under the
Apache 2.0 license in November, 2015 and are available at www.tensorflow.org.

/pdf/tensorflow-large-scale-machine-learning-on-heterogeneous-2yk4fgchw6.pdf

TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments. TensorFlow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general-purpose GPUs, and custom designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexibility to the application developer: whereas in previous "parameter server" designs the management of shared state is built into the system, TensorFlow enables developers to experiment with novel optimizations and training algorithms. TensorFlow supports a variety of applications, with particularly strong support for training and inference on deep neural networks. Several Google services use TensorFlow in production, we have released it as an open-source project, and it has become widely used for machine learning research. In this paper, we describe the TensorFlow dataflow model in contrast to existing systems, and demonstrate the compelling performance that TensorFlow achieves for several real-world applications.

TensorFlow: A system for large-scale machine learning

Robust and powerful software instrumentation tools are essential for program analysis tasks such as profiling, performance evaluation, and bug detection. To meet this need, we have developed a new instrumentation system called Pin. Our goals are to provide easy-to-use, portable, transparent, and efficient instrumentation. Instrumentation tools (called Pintools) are written in C/C++ using Pin's rich API. Pin follows the model of ATOM, allowing the tool writer to analyze an application at the instruction level without the need for detailed knowledge of the underlying instruction set. The API is designed to be architecture independent whenever possible, making Pintools source compatible across different architectures. However, a Pintool can access architecture-specific details when necessary. Instrumentation with Pin is mostly transparent as the application and Pintool observe the application's original, uninstrumented behavior. Pin uses dynamic compilation to instrument executables while they are running. For efficiency, Pin uses several techniques, including inlining, register re-allocation, liveness analysis, and instruction scheduling to optimize instrumentation. This fully automated approach delivers significantly better instrumentation performance than similar tools. For example, Pin is 3.3x faster than Valgrind and 2x faster than DynamoRIO for basic-block counting. To illustrate Pin's versatility, we describe two Pintools in daily use to analyze production software. Pin is publicly available for Linux platforms on four architectures: IA32 (32-bit x86), EM64T (64-bit x86), Itanium®, and ARM. In the ten months since Pin 2 was released in July 2004, there have been over 3000 downloads from its website.

/pdf/pin-building-customized-program-analysis-tools-with-dynamic-1cvwqey5sb.pdf

Pin: building customized program analysis tools with dynamic instrumentation

This paper shows how to build a sparse tensor algebra compiler that is agnostic to tensor formats (data layouts). We develop an interface that describes formats in terms of their capabilities and properties, and show how to build a modular code generator where new formats can be added as plugins. We then describe six implementations of the interface that compose to form the dense, CSR/CSF, COO, DIA, ELL, and HASH tensor formats and countless variants thereof. With these implementations at hand, our code generator can generate code for any tensor algebra expression on any combination of the aforementioned formats. To demonstrate our modular code generator design, we have implemented it in the open-source taco tensor algebra compiler. Our evaluation shows that we get better performance by supporting more formats specialized to different tensor structures, and our plugins makes it easy to add new formats. For example, when data is provided in the COO format, computing a single matrix-vector multiplication with COO is up to 3.6× faster than with CSR. Furthermore, DIA is specialized to tensor convolutions and stencil operations and therefore performs up to 22% faster than CSR for such operations. To further demonstrate the importance of support for many formats, we show that the best vector format for matrix-vector multiplication varies with input sparsities, from hash maps to sparse vectors to dense vectors. Finally, we show that the performance of generated code for these formats is competitive with hand-optimized implementations.

Unified Sparse Formats for Tensor Algebra Compilers.

We present a new algorithm for transposing sparse tensors called Quesadilla. The algorithm converts the sparse tensor data structure to a list of coordinates and sorts it with a fast multi-pass radix algorithm that exploits knowledge of the requested transposition and the tensors input partial coordinate ordering to provably minimize the number of parallel partial sorting passes. We evaluate both a serial and a parallel implementation of Quesadilla on a set of 19 tensors from the FROSTT collection, a set of tensors taken from scientific and data analytic applications. We compare Quesadilla and a generalization, Top-2-sadilla to several state of the art approaches, including the tensor transposition routine used in the SPLATT tensor factorization library. In serial tests, Quesadilla was the best strategy for 60% of all tensor and transposition combinations and improved over SPLATT by at least 19% in half of the combinations. In parallel tests, at least one of Quesadilla or Top-2-sadilla was the best strategy for 52% of all tensor and transposition combinations.

Sparse Tensor Transpositions

The simplest implementation of a domain-specific language is to embed it in an existing language using operator overloading. This way, the DSL can inherit parsing, syntax and type checking, error handling, and the toolchain of debuggers and IDEs from the host language. A natural host language choice for most high-performance DSLs is the de-facto highperformance language, C++. However, DSL designers quickly run into the problem of not being able to extract control flows due to a lack of introspection in C++ and have to resort to special functions with lambdas to represent loops and conditionals. This approach introduces unnecessary syntax and does not capture the side effects of updates inside the lambdas in a safe way. We present BuildIt, a type-based multi-stage execution framework that solves this problem by extracting all control flow operators like if-then-else conditionals and for and while loops using a pure library approach. BuildIt achieves this by repeated execution of the program to explore all control flow paths and construct the AST piece by piece. We show that BuildIt can do this without exponential blow-up in terms of output size and execution time. We apply BuildIt's staging capabilities to the state-of-the-art tensor compiler TACO to generate low-level IR for custom-level formats. Thus, BuildIt offers a way to get both generalization and programmability for the user while generating specialized and efficient code. We also demonstrate that BuildIt can generate rich control-flow from relatively simple code by using it to stage an interpreter for an esoteric language. BuildIt changes the way we think about multi-staging as a problem by reducing the PL complexity of a supposedly harder problem requiring features like introspection or specialized compiler support to a set of common features found in most languages.

BuildIt: a type-based multi-stage programming framework for code generation in C++

ion Layers for Scalable Microfluidic Biocomputers (Extended Version∗) William Thies†, John Paul Urbanski§, Todd Thorsen§, and Saman Amarasinghe† †MIT Computer Science and Artificial Intelligence Laboratory §MIT Hatsopoulos Microfluids Laboratory {thies, urbanski, thorsen, saman}@mit.edu

/pdf/abstraction-layers-for-scalable-microfluidic-biocomputers-3shj8eoz5p.pdf

Abstraction Layers for Scalable Microfluidic Biocomputers (Extended Version)

A runtime code manipulation system is provided that supports code transformations on a program while it executes. The runtime code manipulation system uses code caching technology to provide efficient and comprehensive manipulation of an application running on an operating system and hardware. The code cache includes a system for automatically keeping the code cache at an appropriate size for the current working set of an application running.

Saman Amarasinghe

Papers

Unified Sparse Formats for Tensor Algebra Compilers.

Sparse Tensor Transpositions

BuildIt: a type-based multi-stage programming framework for code generation in C++

Abstraction Layers for Scalable Microfluidic Biocomputers (Extended Version)

Adaptive cache sizing based on monitoring of regenerated and replaced cache entries