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

/pdf/ithemal-accurate-portable-and-fast-basic-block-throughput-19o5lw03vp.pdf

Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks

In this position paper, we describe our vision of the future of machine programming through a categorical examination of three pillars of research. Those pillars are: (i) intention, (ii) invention, and (iii) adaptation. Intention emphasizes advancements in the human-to-computer and computer-to-machine-learning interfaces. Invention emphasizes the creation or refinement of algorithms or core hardware and software building blocks through machine learning (ML). Adaptation emphasizes advances in the use of ML-based constructs to autonomously evolve software.

/pdf/the-three-pillars-of-machine-programming-35vs3rxtm2.pdf

The three pillars of machine programming

The Internet has the potential to deliver information to communities around the world that have no other information resources. High telephone and ISP fees in combination with lowbandwidth connections make it unaffordable for many people to browse the Web online. We are developing the TEK system to enable users to search the Web using only email. TEK stands for "Time Equals Knowledge," since the user exchanges time (waiting for email) for knowledge. The system contains three components: 1) the client, which provides a graphical interface for the end user, 2) the server, which performs the searches from MIT, and 3) a reliable email-based communication protocol between the client and the server. The TEK search engine differs from others in that it is designed to return low-bandwidth results, which are achieved by special filtering, analysis, and compression on the server side. We believe that TEK will bring Web resources to people who otherwise would not be able to afford them.

http://groups.csail.mit.edu/commit/papers/02/www2002-tek.pdf

Searching the World Wide Web in Low-Connectivity Communities

We present Softspec, a technique for parallelizing sequential applications using only simple software mechanisms, requiring no complex program analysis or hardware support. Softspec parallelizes loops whose memory references are stride-predictable. By detecting and speculatively executing potential parallelism at runtime Softspec succeeds in parallelizing loops whose memory access patterns are statically indeterminable. For example, Softspec can parallelize while loops with un-analyzable exit conditions, linked list traversals, and sparse matrix applications with predictable memory patterns. We show performance results using our prototype implementation.

/pdf/softspec-software-based-speculative-parallelism-1vjurobrif.pdf

Softspec: Software-based Speculative Parallelism

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

Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks

The three pillars of machine programming

Searching the World Wide Web in Low-Connectivity Communities

Softspec: Software-based Speculative Parallelism

Adaptive cache sizing