4,396 research outputs found
Packet Transactions: High-level Programming for Line-Rate Switches
Many algorithms for congestion control, scheduling, network measurement,
active queue management, security, and load balancing require custom processing
of packets as they traverse the data plane of a network switch. To run at line
rate, these data-plane algorithms must be in hardware. With today's switch
hardware, algorithms cannot be changed, nor new algorithms installed, after a
switch has been built.
This paper shows how to program data-plane algorithms in a high-level
language and compile those programs into low-level microcode that can run on
emerging programmable line-rate switching chipsets. The key challenge is that
these algorithms create and modify algorithmic state. The key idea to achieve
line-rate programmability for stateful algorithms is the notion of a packet
transaction : a sequential code block that is atomic and isolated from other
such code blocks. We have developed this idea in Domino, a C-like imperative
language to express data-plane algorithms. We show with many examples that
Domino provides a convenient and natural way to express sophisticated
data-plane algorithms, and show that these algorithms can be run at line rate
with modest estimated die-area overhead.Comment: 16 page
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Automatic data/program partitioning using the single assignment principle
Loosely-coupled MIMD architectures do not suffer from memory contention; hence large numbers of processors may be utilized. The main problem, however, is how to partition data and programs in order to exploit the available parallelism. In this paper we show that efficient schemes for automatic data/program partitioning and synchronization may be employed if single assignment is used. Using simulations of program loops common to scientific computations (the Livermore Loops), we demonstrate that only a small fraction of data accesses are remote and thus the degradation in network performance due to multiprocessing is minimal
Speculative Staging for Interpreter Optimization
Interpreters have a bad reputation for having lower performance than
just-in-time compilers. We present a new way of building high performance
interpreters that is particularly effective for executing dynamically typed
programming languages. The key idea is to combine speculative staging of
optimized interpreter instructions with a novel technique of incrementally and
iteratively concerting them at run-time.
This paper introduces the concepts behind deriving optimized instructions
from existing interpreter instructions---incrementally peeling off layers of
complexity. When compiling the interpreter, these optimized derivatives will be
compiled along with the original interpreter instructions. Therefore, our
technique is portable by construction since it leverages the existing
compiler's backend. At run-time we use instruction substitution from the
interpreter's original and expensive instructions to optimized instruction
derivatives to speed up execution.
Our technique unites high performance with the simplicity and portability of
interpreters---we report that our optimization makes the CPython interpreter up
to more than four times faster, where our interpreter closes the gap between
and sometimes even outperforms PyPy's just-in-time compiler.Comment: 16 pages, 4 figures, 3 tables. Uses CPython 3.2.3 and PyPy 1.
Coarse-grained reconfigurable array architectures
Coarse-Grained ReconïŹgurable Array (CGRA) architectures accelerate the same inner loops that beneïŹt from the high ILP support in VLIW architectures. By executing non-loop code on other cores, however, CGRAs can focus on such loops to execute them more efïŹciently. This chapter discusses the basic principles of CGRAs, and the wide range of design options available to a CGRA designer, covering a large number of existing CGRA designs. The impact of different options on ïŹexibility, performance, and power-efïŹciency is discussed, as well as the need for compiler support. The ADRES CGRA design template is studied in more detail as a use case to illustrate the need for design space exploration, for compiler support and for the manual ïŹne-tuning of source code
Survey on Combinatorial Register Allocation and Instruction Scheduling
Register allocation (mapping variables to processor registers or memory) and
instruction scheduling (reordering instructions to increase instruction-level
parallelism) are essential tasks for generating efficient assembly code in a
compiler. In the last three decades, combinatorial optimization has emerged as
an alternative to traditional, heuristic algorithms for these two tasks.
Combinatorial optimization approaches can deliver optimal solutions according
to a model, can precisely capture trade-offs between conflicting decisions, and
are more flexible at the expense of increased compilation time.
This paper provides an exhaustive literature review and a classification of
combinatorial optimization approaches to register allocation and instruction
scheduling, with a focus on the techniques that are most applied in this
context: integer programming, constraint programming, partitioned Boolean
quadratic programming, and enumeration. Researchers in compilers and
combinatorial optimization can benefit from identifying developments, trends,
and challenges in the area; compiler practitioners may discern opportunities
and grasp the potential benefit of applying combinatorial optimization
GeantV: Results from the prototype of concurrent vector particle transport simulation in HEP
Full detector simulation was among the largest CPU consumer in all CERN
experiment software stacks for the first two runs of the Large Hadron Collider
(LHC). In the early 2010's, the projections were that simulation demands would
scale linearly with luminosity increase, compensated only partially by an
increase of computing resources. The extension of fast simulation approaches to
more use cases, covering a larger fraction of the simulation budget, is only
part of the solution due to intrinsic precision limitations. The remainder
corresponds to speeding-up the simulation software by several factors, which is
out of reach using simple optimizations on the current code base. In this
context, the GeantV R&D project was launched, aiming to redesign the legacy
particle transport codes in order to make them benefit from fine-grained
parallelism features such as vectorization, but also from increased code and
data locality. This paper presents extensively the results and achievements of
this R&D, as well as the conclusions and lessons learnt from the beta
prototype.Comment: 34 pages, 26 figures, 24 table
A Tracing JIT Compiler for Erlang using LLVM
We have modified the Erlang runtime to add support for a tracing just-in-time (JIT) compiler, similar to Mozillaâs TraceMonkey. Tracing is a technique to augment an existing interpreter with a JIT simply by recording the instructions executed during a loop iteration, and then generate optimized native code from this. Tracing compilers are particularly suited to optimize number crunching tight loops, an area where Erlang traditionally has been lacking. We make use of the LLVM compiler library to optimize and emit native code. In micro benchmarks we show some major improvements, reducing execution time by up to 75%. However, from an engineering point of view, we conclude that the effort of an industrial strength implementation would be substantial â essentially reimplementing large parts of Erlangâs interpreter â and discuss a potential solution based on recent research in the area.NĂ€stan alla moderna programsprĂ„k anvĂ€nder en interpretator â en flexibel och praktisk om Ă€n lĂ„ngsam lösning. Vi prövar ett enkelt sĂ€tt att kraftigt öka prestandan pĂ„ Erlangs interpretator
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