Dynamic Information Flow Tracking on Multicores

Dynamic Information Flow Tracking (DIFT) is a promising technique for detecting software attacks. Due to the computationally intensive nature of the technique, prior efficient implementations [21, 6] rely on specialized hardware support whose only purpose is to enable DIFT. Alternatively, prior software implementations are either too slow [17, 15] resulting in execution time increases as much as four fold for SPEC integer programs or they are not transparent [31] requiring source code modifications. In this paper, we propose the use of chip multiprocessors (CMP) to perform DIFT transparently and efficiently. We spawn a helper thread that is scheduled on a separate core and is only responsible for performing information flow tracking operations. This entails the communication of registers and flags between the main and helper threads. We explore software (shared memory) and hardware (dedicated interconnect) approaches to enable this communication. Finally, we propose a novel application of the DIFT infrastructure where, in addition to the detection of the software attack, DIFT assists in the process of identifying the cause of the bug in the code that enabled the exploit in the first place. We conducted detailed simulations to evaluate the overhead for performing DIFT and found that to be 48 % for SPEC integer programs

Scratchpad Management in Software Managed Manycore Architectures

abstract: Caches have long been used to reduce memory access latency. However, the increased complexity of cache coherence brings significant challenges in processor design as the number of cores increases. While making caches scalable is still an important research problem, some researchers are exploring the possibility of a more power-efficient SRAM called scratchpad memories or SPMs. SPMs consume significantly less area, and are more energy-efficient per access than caches, and therefore make the design of on-chip memories much simpler. Unlike caches, which fetch data from memories automatically, an SPM requires explicit instructions for data transfers. SPM-only architectures are thus named as software managed manycore (SMM), since the data movements of such architectures rely on software. SMM processors have been widely used in different areas, such as embedded computing, network processing, or even high performance computing. While SMM processors provide a low-power platform, the hardware alone does not guarantee power efficiency, if applications on such processors deliver low performance. Efficient software techniques are therefore required. A big body of management techniques for SMM architectures are compiler-directed, as inserting data movement operations by hand forces programmers to trace flow of data, which can be error-prone and sometimes difficult if not impossible. This thesis develops compiler-directed techniques to manage data transfers for embedded applications on SMMs efficiently. The techniques analyze and find out the proper program points and insert data movement instructions accordingly. The techniques manage code, stack and heap data of applications, and reduce execution time by 14%, 52% and 80% respectively compared to their predecessors on typical embedded applications. On top of managing local data, a technique is also developed for shared data in SMM architectures. Experimental results show it achieves more than 2X speedup than the previous technique on average.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Application-aware Performance Optimization for Software Managed Manycore Architectures

abstract: One of the main goals of computer architecture design is to improve performance without much increase in the power consumption. It cannot be achieved by adding increasingly complex intelligent schemes in the hardware, since they will become increasingly less power-efficient. Therefore, parallelism comes up as the solution. In fact, the irrevocable trend of computer design in near future is still to keep increasing the number of cores while reducing the operating frequency. However, it is not easy to scale number of cores. One important challenge is that existing cores consume too much power. Another challenge is that cache-based memory hierarchy poses a serious limitation due to the rapidly increasing demand of area and power for coherence maintenance. In this dissertation, opportunities to resolve the aforementioned issues were explored in two aspects. Firstly, the possibility of removing hardware cache altogether, and replacing it with scratchpad memory with software management was explored. Scratchpad memory consumes much less power than caches. However, as data management logic is completely shifted to Software, how to reduce software overhead is challenging. This thesis presents techniques to manage scratchpad memory judiciously by exploiting application semantics and knowledge of data access patterns, thereby enabling optimization of data movement across the memory hierarchy. Experimental results show that the optimization was able to reduce stack data management overhead by 13X, produce better code mapping in more than 80% of the case, and improve performance by 83% in heap management. Secondly, the possibility of using software branch hinting to replace hardware branch prediction to completely eliminate power consumption on corresponding hardware components was explored. As branch predictor is removed from hardware, software logic is responsible for reducing branch penalty. Techniques to minimize the branch penalty by optimizing branch hint placement were proposed, which can reduce branch penalty by 35.4% over the state-of-the-art.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Enhancing Productivity and Performance Portability of General-Purpose Parallel Programming

This work focuses on compiler and run-time techniques for improving the productivity and the performance portability of general-purpose parallel programming. More specifically, we focus on shared-memory task-parallel languages, where the programmer explicitly exposes parallelism in the form of short tasks that may outnumber the cores by orders of magnitude. The compiler, the run-time, and the platform (henceforth the system) are responsible for harnessing this unpredictable amount of parallelism, which can vary from none to excessive, towards efficient execution. The challenge arises from the aspiration to support fine-grained irregular computations and nested parallelism. This work is even more ambitious by also aspiring to lay the foundations to efficiently support declarative code, where the programmer exposes all available parallelism, using high-level language constructs such as parallel loops, reducers or futures. The appeal of declarative code is twofold for general-purpose programming: it is often easier for the programmer who does not have to worry about the granularity of the exposed parallelism, and it achieves better performance portability by avoiding overfitting to a small range of platforms and inputs for which the programmer is coarsening. Furthermore, PRAM algorithms, an important class of parallel algorithms, naturally lend themselves to declarative programming, so supporting it is a necessary condition for capitalizing on the wealth of the PRAM theory. Unfortunately, declarative codes often expose such an overwhelming number of fine-grained tasks that existing systems fail to deliver performance. Our contributions can be partitioned into three components. First, we tackle the issue of coarsening, which declarative code leaves to the system. We identify two goals of coarsening and advocate tackling them separately, using static compiler transformations for one and dynamic run-time approaches for the other. Additionally, we present evidence that the current practice of burdening the programmer with coarsening either leads to codes with poor performance-portability, or to a significantly increased programming effort. This is a ``show-stopper'' for general-purpose programming. To compare the performance portability among approaches, we define an experimental framework and two metrics, and we demonstrate that our approaches are preferable. We close the chapter on coarsening by presenting compiler transformations that automatically coarsen some types of very fine-grained codes. Second, we propose Lazy Scheduling, an innovative run-time scheduling technique that infers the platform load at run-time, using information already maintained. Based on the inferred load, Lazy Scheduling adapts the amount of available parallelism it exposes for parallel execution and, thus, saves parallelism overheads that existing approaches pay. We implement Lazy Scheduling and present experimental results on four different platforms. The results show that Lazy Scheduling is vastly superior for declarative codes and competitive, if not better, for coarsened codes. Moreover, Lazy Scheduling is also superior in terms of performance-portability, supporting our thesis that it is possible to achieve reasonable efficiency and performance portability with declarative codes. Finally, we also implement Lazy Scheduling on XMT, an experimental manycore platform developed at the University of Maryland, which was designed to support codes derived from PRAM algorithms. On XMT, we manage to harness the existing hardware support for scheduling flat parallelism to compose it with Lazy Scheduling, which supports nested parallelism. In the resulting hybrid scheduler, the hardware and software work in synergy to overcome each other's weaknesses. We show the performance composability of the hardware and software schedulers, both in an abstract cost model and experimentally, as the hybrid always performs better than the software scheduler alone. Furthermore, the cost model is validated by using it to predict if it is preferable to execute a code sequentially, with outer parallelism, or with nested parallelism, depending on the input, the available hardware parallelism and the calling context of the parallel code

Compiler and Runtime for Memory Management on Software Managed Manycore Processors

abstract: We are expecting hundreds of cores per chip in the near future. However, scaling the memory architecture in manycore architectures becomes a major challenge. Cache coherence provides a single image of memory at any time in execution to all the cores, yet coherent cache architectures are believed will not scale to hundreds and thousands of cores. In addition, caches and coherence logic already take 20-50% of the total power consumption of the processor and 30-60% of die area. Therefore, a more scalable architecture is needed for manycore architectures. Software Managed Manycore (SMM) architectures emerge as a solution. They have scalable memory design in which each core has direct access to only its local scratchpad memory, and any data transfers to/from other memories must be done explicitly in the application using Direct Memory Access (DMA) commands. Lack of automatic memory management in the hardware makes such architectures extremely power-efficient, but they also become difficult to program. If the code/data of the task mapped onto a core cannot fit in the local scratchpad memory, then DMA calls must be added to bring in the code/data before it is required, and it may need to be evicted after its use. However, doing this adds a lot of complexity to the programmer's job. Now programmers must worry about data management, on top of worrying about the functional correctness of the program - which is already quite complex. This dissertation presents a comprehensive compiler and runtime integration to automatically manage the code and data of each task in the limited local memory of the core. We firstly developed a Complete Circular Stack Management. It manages stack frames between the local memory and the main memory, and addresses the stack pointer problem as well. Though it works, we found we could further optimize the management for most cases. Thus a Smart Stack Data Management (SSDM) is provided. In this work, we formulate the stack data management problem and propose a greedy algorithm for the same. Later on, we propose a general cost estimation algorithm, based on which CMSM heuristic for code mapping problem is developed. Finally, heap data is dynamic in nature and therefore it is hard to manage it. We provide two schemes to manage unlimited amount of heap data in constant sized region in the local memory. In addition to those separate schemes for different kinds of data, we also provide a memory partition methodology.Dissertation/ThesisPh.D. Computer Science 201

WCET-Aware Scratchpad Memory Management for Hard Real-Time Systems

abstract: Cyber-physical systems and hard real-time systems have strict timing constraints that specify deadlines until which tasks must finish their execution. Missing a deadline can cause unexpected outcome or endanger human lives in safety-critical applications, such as automotive or aeronautical systems. It is, therefore, of utmost importance to obtain and optimize a safe upper bound of each task’s execution time or the worst-case execution time (WCET), to guarantee the absence of any missed deadline. Unfortunately, conventional microarchitectural components, such as caches and branch predictors, are only optimized for average-case performance and often make WCET analysis complicated and pessimistic. Caches especially have a large impact on the worst-case performance due to expensive off- chip memory accesses involved in cache miss handling. In this regard, software-controlled scratchpad memories (SPMs) have become a promising alternative to caches. An SPM is a raw SRAM, controlled only by executing data movement instructions explicitly at runtime, and such explicit control facilitates static analyses to obtain safe and tight upper bounds of WCETs. SPM management techniques, used in compilers targeting an SPM-based processor, determine how to use a given SPM space by deciding where to insert data movement instructions and what operations to perform at those program locations. This dissertation presents several management techniques for program code and stack data, which aim to optimize the WCETs of a given program. The proposed code management techniques include optimal allocation algorithms and a polynomial-time heuristic for allocating functions to the SPM space, with or without the use of abstraction of SPM regions, and a heuristic for splitting functions into smaller partitions. The proposed stack data management technique, on the other hand, finds an optimal set of program locations to evict and restore stack frames to avoid stack overflows, when the call stack resides in a size-limited SPM. In the evaluation, the WCETs of various benchmarks including real-world automotive applications are statically calculated for SPMs and caches in several different memory configurations.Dissertation/ThesisDoctoral Dissertation Computer Science 201

The potential of programmable logic in the middle: cache bleaching

Consolidating hard real-time systems onto modern multi-core Systems-on-Chip (SoC) is an open challenge. The extensive sharing of hardware resources at the memory hierarchy raises important unpredictability concerns. The problem is exacerbated as more computationally demanding workload is expected to be handled with real-time guarantees in next-generation Cyber-Physical Systems (CPS). A large body of works has approached the problem by proposing novel hardware re-designs, and by proposing software-only solutions to mitigate performance interference. Strong from the observation that unpredictability arises from a lack of fine-grained control over the behavior of shared hardware components, we outline a promising new resource management approach. We demonstrate that it is possible to introduce Programmable Logic In-the-Middle (PLIM) between a traditional multi-core processor and main memory. This provides the unique capability of manipulating individual memory transactions. We propose a proof-of-concept system implementation of PLIM modules on a commercial multi-core SoC. The PLIM approach is then leveraged to solve long-standing issues with cache coloring. Thanks to PLIM, colored sparse addresses can be re-compacted in main memory. This is the base principle behind the technique we call Cache Bleaching. We evaluate our design on real applications and propose hypervisor-level adaptations to showcase the potential of the PLIM approach.Accepted manuscrip

Management of Scratchpad Memory Using Programming Techniques

Consuming the conventional approaches, processors are incapable to achieve effective energy reduction. In upcoming processors on-chip memory system will be the major restriction. On-chip memories are managed by the software SMCs (Software Managed Chips), and are work with caches (on-chip), where inside a block of caches software can explicitly read as well as write specific or complete memory references, or work separately just like scratchpad memory. In embedded systems Scratch memory is generally used as an addition to caches or as a substitute of cache, but due to their comprehensive ease of programmability cache containing architectures are still to be chosen in numerous applications. In contrast to conventional caches in embedded schemes because of their better energy and silicon range effectiveness SPM (Scratch-Pad Memories) are being progressively used. Power consumption of ported applications can significantly be lower as well as portability of scratchpad architectures will be advanced with the language agnostic software management method which is suggested in this manuscript. To enhance the memory configuration and optimization on relevant architectures based on SPM, the variety of current methods are reviewed for finding the chances of optimizations and usage of new methods as well as their applicability to numerous schemes of memory management are also discussed in this paper