Intel(R) Software Guard Extensions Developer Reference for Linux* OS
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2.5 Classification of Parallel Computers
52 // Architectures 2.5 Classification of Parallel Computers 2.5 Classification of Parallel Computers 2.5.1 Granularity In parallel computing, granularity means the amount of computation in relation to communication or synchronisation Periods of computation are typically separated from periods of communication by synchronization events. • fine level (same operations with different data) ◦ vector processors ◦ instruction level parallelism ◦ fine-grain parallelism: – Relatively small amounts of computational work are done between communication events – Low computation to communication ratio – Facilitates load balancing 53 // Architectures 2.5 Classification of Parallel Computers – Implies high communication overhead and less opportunity for per- formance enhancement – If granularity is too fine it is possible that the overhead required for communications and synchronization between tasks takes longer than the computation. • operation level (different operations simultaneously) • problem level (independent subtasks) ◦ coarse-grain parallelism: – Relatively large amounts of computational work are done between communication/synchronization events – High computation to communication ratio – Implies more opportunity for performance increase – Harder to load balance efficiently 54 // Architectures 2.5 Classification of Parallel Computers 2.5.2 Hardware: Pipelining (was used in supercomputers, e.g. Cray-1) In N elements in pipeline and for 8 element L clock cycles =) for calculation it would take L + N cycles; without pipeline L ∗ N cycles Example of good code for pipelineing: §doi =1 ,k ¤ z ( i ) =x ( i ) +y ( i ) end do ¦ 55 // Architectures 2.5 Classification of Parallel Computers Vector processors, fast vector operations (operations on arrays). Previous example good also for vector processor (vector addition) , but, e.g. recursion – hard to optimise for vector processors Example: IntelMMX – simple vector processor. -
SIMD Extensions
SIMD Extensions PDF generated using the open source mwlib toolkit. See http://code.pediapress.com/ for more information. PDF generated at: Sat, 12 May 2012 17:14:46 UTC Contents Articles SIMD 1 MMX (instruction set) 6 3DNow! 8 Streaming SIMD Extensions 12 SSE2 16 SSE3 18 SSSE3 20 SSE4 22 SSE5 26 Advanced Vector Extensions 28 CVT16 instruction set 31 XOP instruction set 31 References Article Sources and Contributors 33 Image Sources, Licenses and Contributors 34 Article Licenses License 35 SIMD 1 SIMD Single instruction Multiple instruction Single data SISD MISD Multiple data SIMD MIMD Single instruction, multiple data (SIMD), is a class of parallel computers in Flynn's taxonomy. It describes computers with multiple processing elements that perform the same operation on multiple data simultaneously. Thus, such machines exploit data level parallelism. History The first use of SIMD instructions was in vector supercomputers of the early 1970s such as the CDC Star-100 and the Texas Instruments ASC, which could operate on a vector of data with a single instruction. Vector processing was especially popularized by Cray in the 1970s and 1980s. Vector-processing architectures are now considered separate from SIMD machines, based on the fact that vector machines processed the vectors one word at a time through pipelined processors (though still based on a single instruction), whereas modern SIMD machines process all elements of the vector simultaneously.[1] The first era of modern SIMD machines was characterized by massively parallel processing-style supercomputers such as the Thinking Machines CM-1 and CM-2. These machines had many limited-functionality processors that would work in parallel. -
The Case for Hardware Transactional Memory
Lecture 20: Transactional Memory CMU 15-418: Parallel Computer Architecture and Programming (Spring 2012) Giving credit ▪ Many of the slides in today’s talk are the work of Professor Christos Kozyrakis (Stanford University) (CMU 15-418, Spring 2012) Raising level of abstraction for synchronization ▪ Machine-level synchronization prims: - fetch-and-op, test-and-set, compare-and-swap ▪ We used these primitives to construct higher level, but still quite basic, synchronization prims: - lock, unlock, barrier ▪ Today: - transactional memory: higher level synchronization (CMU 15-418, Spring 2012) What you should know ▪ What a transaction is ▪ The difference between the atomic construct and locks ▪ Design space of transactional memory implementations - data versioning policy - con"ict detection policy - granularity of detection ▪ Understand HW implementation of transaction memory (consider how it relates to coherence protocol implementations we’ve discussed in the past) (CMU 15-418, Spring 2012) Example void deposit(account, amount) { lock(account); int t = bank.get(account); t = t + amount; bank.put(account, t); unlock(account); } ▪ Deposit is a read-modify-write operation: want “deposit” to be atomic with respect to other bank operations on this account. ▪ Lock/unlock pair is one mechanism to ensure atomicity (ensures mutual exclusion on the account) (CMU 15-418, Spring 2012) Programming with transactional memory void deposit(account, amount){ void deposit(account, amount){ lock(account); atomic { int t = bank.get(account); int t = bank.get(account); t = t + amount; t = t + amount; bank.put(account, t); bank.put(account, t); unlock(account); } } } nDeclarative synchronization nProgrammers says what but not how nNo explicit declaration or management of locks nSystem implements synchronization nTypically with optimistic concurrency nSlow down only on true con"icts (R-W or W-W) (CMU 15-418, Spring 2012) Declarative vs. -
The Von Neumann Computer Model 5/30/17, 10:03 PM
The von Neumann Computer Model 5/30/17, 10:03 PM CIS-77 Home http://www.c-jump.com/CIS77/CIS77syllabus.htm The von Neumann Computer Model 1. The von Neumann Computer Model 2. Components of the Von Neumann Model 3. Communication Between Memory and Processing Unit 4. CPU data-path 5. Memory Operations 6. Understanding the MAR and the MDR 7. Understanding the MAR and the MDR, Cont. 8. ALU, the Processing Unit 9. ALU and the Word Length 10. Control Unit 11. Control Unit, Cont. 12. Input/Output 13. Input/Output Ports 14. Input/Output Address Space 15. Console Input/Output in Protected Memory Mode 16. Instruction Processing 17. Instruction Components 18. Why Learn Intel x86 ISA ? 19. Design of the x86 CPU Instruction Set 20. CPU Instruction Set 21. History of IBM PC 22. Early x86 Processor Family 23. 8086 and 8088 CPU 24. 80186 CPU 25. 80286 CPU 26. 80386 CPU 27. 80386 CPU, Cont. 28. 80486 CPU 29. Pentium (Intel 80586) 30. Pentium Pro 31. Pentium II 32. Itanium processor 1. The von Neumann Computer Model Von Neumann computer systems contain three main building blocks: The following block diagram shows major relationship between CPU components: the central processing unit (CPU), memory, and input/output devices (I/O). These three components are connected together using the system bus. The most prominent items within the CPU are the registers: they can be manipulated directly by a computer program. http://www.c-jump.com/CIS77/CPU/VonNeumann/lecture.html Page 1 of 15 IPR2017-01532 FanDuel, et al. -
Robust Architectural Support for Transactional Memory in the Power Architecture
Robust Architectural Support for Transactional Memory in the Power Architecture Harold W. Cain∗ Brad Frey Derek Williams IBM Research IBM STG IBM STG Yorktown Heights, NY, USA Austin, TX, USA Austin, TX, USA [email protected] [email protected] [email protected] Maged M. Michael Cathy May Hung Le IBM Research IBM Research (retired) IBM STG Yorktown Heights, NY, USA Yorktown Heights, NY, USA Austin, TX, USA [email protected] [email protected] [email protected] ABSTRACT in current p795 systems, with 8 TB of DRAM), as well as On the twentieth anniversary of the original publication [10], strengths in RAS that differentiate it in the market, adding following ten years of intense activity in the research lit- TM must not compromise any of these virtues. A robust erature, hardware support for transactional memory (TM) system is one that is sturdy in construction, a trait that has finally become a commercial reality, with HTM-enabled does not usually come to mind in respect to HTM systems. chips currently or soon-to-be available from many hardware We structured TM to work in harmony with features that vendors. In this paper we describe architectural support for support the architecture's scalability. Our goal has been to TM provide a comprehensive programming environment includ- TM added to a future version of the Power ISA . Two im- ing support for simple system calls and debug aids, while peratives drove the development: the desire to complement providing a robust (in the sense of "no surprises") execu- our weakly-consistent memory model with a more friendly tion environment with reasonably consistent performance interface to simplify the development and porting of multi- and without unexpected transaction failures.2 TM must be threaded applications, and the need for robustness beyond usable throughout the system stack: in hypervisors, oper- that of some early implementations. -
Leveraging Modern Multi-Core Processor Features to Efficiently
LEVERAGING MODERN MULTI-CORE PROCESSORS FEATURES TO EFFICIENTLY DEAL WITH SILENT ERRORS, AUGUST 2017 1 Leveraging Modern Multi-core Processor Features to Efficiently Deal with Silent Errors Diego Perez´ ∗, Thomas Roparsz, Esteban Meneses∗y ∗School of Computing, Costa Rica Institute of Technology yAdvanced Computing Laboratory, Costa Rica National High Technology Center zUniv. Grenoble Alpes, CNRS, Grenoble INP ' , LIG, F-38000 Grenoble France Abstract—Since current multi-core processors are more com- about 200% of resource overhead [3]. Recent contributions [1] plex systems on a chip than previous generations, some transient [4] tend to show that checkpointing techniques provide the errors may happen, go undetected by the hardware and can best compromise between transparency for the developer and potentially corrupt the result of an expensive calculation. Because of that, techniques such as Instruction Level Redundancy or efficient resource usage. In theses cases only one extra copy checkpointing are utilized to detect and correct these soft errors; is executed, which is enough to detect the error. But, still the however these mechanisms are highly expensive, adding a lot technique remains expensive with about 100% of overhead. of resource overhead. Hardware Transactional Memory (HTM) To reduce this overhead, some features provided by recent exposes a very convenient and efficient way to revert the state of processors, such as hardware-managed transactions, Hyper- a core’s cache, which can be utilized as a recovery technique. An experimental prototype has been created that uses such feature Threading and memory protection extensions, could be great to recover the previous state of the calculation when a soft error opportunities to implement efficient error detection and recov- has been found. -
Lecture Notes
Lecture #4-5: Computer Hardware (Overview and CPUs) CS106E Spring 2018, Young In these lectures, we begin our three-lecture exploration of Computer Hardware. We start by looking at the different types of computer components and how they interact during basic computer operations. Next, we focus specifically on the CPU (Central Processing Unit). We take a look at the Machine Language of the CPU and discover it’s really quite primitive. We explore how Compilers and Interpreters allow us to go from the High-Level Languages we are used to programming to the Low-Level machine language actually used by the CPU. Most modern CPUs are multicore. We take a look at when multicore provides big advantages and when it doesn’t. We also take a short look at Graphics Processing Units (GPUs) and what they might be used for. We end by taking a look at Reduced Instruction Set Computing (RISC) and Complex Instruction Set Computing (CISC). Stanford President John Hennessy won the Turing Award (Computer Science’s equivalent of the Nobel Prize) for his work on RISC computing. Hardware and Software: Hardware refers to the physical components of a computer. Software refers to the programs or instructions that run on the physical computer. - We can entirely change the software on a computer, without changing the hardware and it will transform how the computer works. I can take an Apple MacBook for example, remove the Apple Software and install Microsoft Windows, and I now have a Window’s computer. - In the next two lectures we will focus entirely on Hardware. -
Lecture 21: Transactional Memory
Lecture 21: Transactional Memory • Topics: Hardware TM basics, different implementations 1 Transactions • New paradigm to simplify programming . instead of lock-unlock, use transaction begin-end . locks are blocking, transactions execute speculatively in the hope that there will be no conflicts • Can yield better performance; Eliminates deadlocks • Programmer can freely encapsulate code sections within transactions and not worry about the impact on performance and correctness (for the most part) • Programmer specifies the code sections they’d like to see execute atomically – the hardware takes care of the rest (provides illusion of atomicity) 2 Transactions • Transactional semantics: . when a transaction executes, it is as if the rest of the system is suspended and the transaction is in isolation . the reads and writes of a transaction happen as if they are all a single atomic operation . if the above conditions are not met, the transaction fails to commit (abort) and tries again transaction begin read shared variables arithmetic write shared variables transaction end 3 Example Producer-consumer relationships – producers place tasks at the tail of a work-queue and consumers pull tasks out of the head Enqueue Dequeue transaction begin transaction begin if (tail == NULL) if (head->next == NULL) update head and tail update head and tail else else update tail update head transaction end transaction end With locks, neither thread can proceed in parallel since head/tail may be updated – with transactions, enqueue and dequeue can proceed in parallel -
An Introduction to Gpus, CUDA and Opencl
An Introduction to GPUs, CUDA and OpenCL Bryan Catanzaro, NVIDIA Research Overview ¡ Heterogeneous parallel computing ¡ The CUDA and OpenCL programming models ¡ Writing efficient CUDA code ¡ Thrust: making CUDA C++ productive 2/54 Heterogeneous Parallel Computing Latency-Optimized Throughput- CPU Optimized GPU Fast Serial Scalable Parallel Processing Processing 3/54 Why do we need heterogeneity? ¡ Why not just use latency optimized processors? § Once you decide to go parallel, why not go all the way § And reap more benefits ¡ For many applications, throughput optimized processors are more efficient: faster and use less power § Advantages can be fairly significant 4/54 Why Heterogeneity? ¡ Different goals produce different designs § Throughput optimized: assume work load is highly parallel § Latency optimized: assume work load is mostly sequential ¡ To minimize latency eXperienced by 1 thread: § lots of big on-chip caches § sophisticated control ¡ To maXimize throughput of all threads: § multithreading can hide latency … so skip the big caches § simpler control, cost amortized over ALUs via SIMD 5/54 Latency vs. Throughput Specificaons Westmere-EP Fermi (Tesla C2050) 6 cores, 2 issue, 14 SMs, 2 issue, 16 Processing Elements 4 way SIMD way SIMD @3.46 GHz @1.15 GHz 6 cores, 2 threads, 4 14 SMs, 48 SIMD Resident Strands/ way SIMD: vectors, 32 way Westmere-EP (32nm) Threads (max) SIMD: 48 strands 21504 threads SP GFLOP/s 166 1030 Memory Bandwidth 32 GB/s 144 GB/s Register File ~6 kB 1.75 MB Local Store/L1 Cache 192 kB 896 kB L2 Cache 1.5 MB 0.75 MB -
Cuda C Programming Guide
CUDA C PROGRAMMING GUIDE PG-02829-001_v10.0 | October 2018 Design Guide CHANGES FROM VERSION 9.0 ‣ Documented restriction that operator-overloads cannot be __global__ functions in Operator Function. ‣ Removed guidance to break 8-byte shuffles into two 4-byte instructions. 8-byte shuffle variants are provided since CUDA 9.0. See Warp Shuffle Functions. ‣ Passing __restrict__ references to __global__ functions is now supported. Updated comment in __global__ functions and function templates. ‣ Documented CUDA_ENABLE_CRC_CHECK in CUDA Environment Variables. ‣ Warp matrix functions now support matrix products with m=32, n=8, k=16 and m=8, n=32, k=16 in addition to m=n=k=16. www.nvidia.com CUDA C Programming Guide PG-02829-001_v10.0 | ii TABLE OF CONTENTS Chapter 1. Introduction.........................................................................................1 1.1. From Graphics Processing to General Purpose Parallel Computing............................... 1 1.2. CUDA®: A General-Purpose Parallel Computing Platform and Programming Model.............3 1.3. A Scalable Programming Model.........................................................................4 1.4. Document Structure...................................................................................... 5 Chapter 2. Programming Model............................................................................... 7 2.1. Kernels......................................................................................................7 2.2. Thread Hierarchy........................................................................................ -
Practical Enclave Malware with Intel SGX
Practical Enclave Malware with Intel SGX Michael Schwarz, Samuel Weiser, Daniel Gruss Graz University of Technology Abstract. Modern CPU architectures offer strong isolation guarantees towards user applications in the form of enclaves. However, Intel's threat model for SGX assumes fully trusted enclaves and there doubt about how realistic this is. In particular, it is unclear to what extent enclave malware could harm a system. In this work, we practically demonstrate the first enclave malware which fully and stealthily impersonates its host application. Together with poorly-deployed application isolation on per- sonal computers, such malware can not only steal or encrypt documents for extortion but also act on the user's behalf, e.g., send phishing emails or mount denial-of-service attacks. Our SGX-ROP attack uses new TSX- based memory-disclosure primitive and a write-anything-anywhere prim- itive to construct a code-reuse attack from within an enclave which is then inadvertently executed by the host application. With SGX-ROP, we bypass ASLR, stack canaries, and address sanitizer. We demonstrate that instead of protecting users from harm, SGX currently poses a se- curity threat, facilitating so-called super-malware with ready-to-hit ex- ploits. With our results, we demystify the enclave malware threat and lay ground for future research on defenses against enclave malware. Keywords: Intel SGX, Trusted Execution Environments, Malware 1 Introduction Software isolation is a long-standing challenge in system security, especially if parts of the system are considered vulnerable, compromised, or malicious [24]. Recent isolated-execution technology such as Intel SGX [23] can shield software modules via hardware protected enclaves even from privileged kernel malware. -
Taming Transactions: Towards Hardware-Assisted Control Flow Integrity Using Transactional Memory
Taming Transactions: Towards Hardware-Assisted Control Flow Integrity Using Transactional Memory B Marius Muench1( ), Fabio Pagani1, Yan Shoshitaishvili2, Christopher Kruegel2, Giovanni Vigna2, and Davide Balzarotti1 1 Eurecom, Sophia Antipolis, France {marius.muench,fabio.pagani,davide.balzarotti}@eurecom.fr 2 University of California, Santa Barbara, USA {yans,chris,vigna}@cs.ucsb.edu Abstract. Control Flow Integrity (CFI) is a promising defense tech- nique against code-reuse attacks. While proposals to use hardware fea- tures to support CFI already exist, there is still a growing demand for an architectural CFI support on commodity hardware. To tackle this problem, in this paper we demonstrate that the Transactional Synchro- nization Extensions (TSX) recently introduced by Intel in the x86-64 instruction set can be used to support CFI. The main idea of our approach is to map control flow transitions into transactions. This way, violations of the intended control flow graphs would then trigger transactional aborts, which constitutes the core of our TSX-based CFI solution. To prove the feasibility of our technique, we designed and implemented two coarse-grained CFI proof-of-concept implementations using the new TSX features. In particular, we show how hardware-supported transactions can be used to enforce both loose CFI (which does not need to extract the control flow graph in advance) and strict CFI (which requires pre-computed labels to achieve a better pre- cision). All solutions are based on a compile-time instrumentation. We evaluate the effectiveness and overhead of our implementations to demonstrate that a TSX-based implementation contains useful concepts for architectural control flow integrity support.