Wire Delay is emerging as the natural limiter to microprocessor scalability. A new architectural approach could solve this problem...

This paper introduces Bitwise, a compiler that minimizes the bitwidth --- the number of bits used to representeach operand --- for both integers and pointers in a program. By propagating static information both forward and backward in the program dataflowgraph,Bitwise frees the programmer from declaring bitwidth invariants in cases where the compiler can determine bitwidths automatically. We find a rich opportunity for bitwidth reduction in modern multimedia and streaming application workloads. For new architectures that support sub-word quantities, we expect that our bitwidth reductions will savepower and increase processor performance. This paper

Reconfigurable hardware has the potential for significant performance improvements by providing support for application−specific operations. We report our experience with Chimaera, a prototype system that integrates a small and fast reconfigurable functional unit (RFU) into the pipeline of an aggressive, dynamically−scheduled superscalar processor. Chimaera is capable of performing 9−input/1−output operations on integer data. We discuss the Chimaera C compiler that automatically maps computations for execution in the RFU. Chimaera is capable of: (1) collapsing a set of instructions into RFU operations, (2) converting control−flow into RFU operations, and (3) supporting a more powerful fine−grain data−parallel model than that supported by current multimedia extension instruction sets (for integer operations). Using a set of multimedia and communication applications we show that even with simple optimizations, the Chimaera C compiler is able to map 22 % of all instructions to the RFU on the average. A variety of computations are mapped into RFU operations ranging from as simple as add/sub−shift pairs to operations of more than 10 instructions including several branches. Timing experiments demonstrate that for a 4−way out−of−order superscalar processor Chimaera results in average performance improvements of 21%, assuming a very aggressive core processor design (most pessimistic RFU latency model) and communication overheads from and to the RFU.

With the increasing miniaturization of transistors, wire delays are becoming a dominant factor in microprocessor performance. To address this issue, a number of emerging architectures contain replicated processing units with software-exposed communication between one unit and another (e.g., Raw, iWarp, SmartMemories). However, for their use to be widespread, it will be necessary to develop compiler technology that enables a portable, high-level language to execute efficiently across a range of wireexposed architectures.

{ottoni, ram, astoler, august}@princeton.edu Abstract Until recently, a steadily rising clock rate and otheruniprocessor microarchitectural improvements could be relied upon to consistently deliver increasing performance fora wide range of applications. Current difficulties in maintaining this trend have lead microprocessor manufacturersto add value by incorporating multiple processors on a chip. Unfortunately, since decades of compiler research have notsucceeded in delivering automatic threading for prevalent code properties, this approach demonstrates no improve-ment for a large class of existing codes. To find useful work for chip multiprocessors, we proposean automatic approach to thread extraction, called Decoupled Software Pipelining (DSWP). DSWP exploits the fine-grained pipeline parallelism lurking in most applications to extract long-running, concurrently executing threads. Useof the non-speculative and truly decoupled threads produced by DSWP can increase execution efficiency and pro-vide significant latency tolerance, mitigating design complexity by reducing inter-core communication and per-coreresource requirements. Using our initial fully automatic compiler implementation and a validated processor model,we prove the concept by demonstrating significant gains for dual-core chip multiprocessor models running a variety ofcodes. We then explore simple opportunities missed by our initial compiler implementation which suggest a promisingfuture for this approach. 1

This paper describes Maps, a compiler managed memory system for Raw architectures. Traditional processors for sequential programs maintain the abstraction of a unified memory by using a single centralized memory system. This implementation leads to the infamous "Von Neumann bottleneck, " with machine performance limited by the large memory latency and limited memory bandwidth. A Raw architecture addresses this problem by taking advantage of the rapidly increasing transistor budget to move much of its memory on chip. To remove the bottleneck and complexity associated with centralized memory, Raw distributes the memory with its processing elements. Unified memory semantics are implemented jointly by the hardware and the compiler. The hardware provides a clean compiler interface to its two inter-tile interconnects: a fast, statically schedulable network and a traditional dynamic network. Maps then uses these communication mechanisms to orchestrate the memory accesses for low latency and parallelism while enforcing proper dependence. It optimizes for speed in two ways: by finding accesses that can be scheduled on the static interconnect through static promotion, and by minimizing dependence sequentialization for the remaining accesses. Static promotion is performed using equivalence class unification and modulo unrolling; memory dependences are enforced through explicit synchronization and software serial ordering. We have implemented Maps based on the SUIF infrastructure. This paper demonstrates that the exclusive use of static promotion yields roughly 20-fold speedup on 32 tiles for our regular applications and about 5-fold speedup on 16 or more tiles for our irregular applications. The paper also shows that selective use of dynamic accesses can be a useful complement to...

Clustered ILP processors are characterized by a large number of non-centralized on-chip resources grouped into clusters. Traditional code generation schemes for these processors consist of multiple phases for cluster assignment, register allocation and instruction scheduling. Most of these approaches need additional re-scheduling phases because they often do not impose finite resource constraints in all phases of code generation. These phase-ordered solutions have several drawbacks, resulting in the generation of poor performance code. Moreover, the iterative/back-tracking algorithms used in some of these schemes have large running times. In this paper we present CARS, a code generation framework for Clustered ILP processors, which combines the cluster assignment, register allocation, and instruction scheduling phases into a single code generation phase, thereby eliminating the problems associated with phase-ordered solutions. The CARS algorithm explicitly takes into account all the resource constraints at each cluster scheduling step to reduce spilling and to avoid iterative re-scheduling steps. We also present a new on-the-fly register allocation scheme developed for CARS. We describe an implementation of the proposed code generation framework and the results of a performance evaluation study using the SPEC95/2000 and MediaBench benchmarks.

Draft - submitted to PACT'00. Do not distribute. Contact authors for final version. Over the past decade the number of transistors available to VLSI chip designers has grown exponentially. While the physical capacity to integrate large systems on a single chip will soon be available, there is currently little agreement regarding the types of architectures and compilation environments that will be appropriate for these new systems. This paper examines systems-on-a-chip with an eye towards system-level adaptability and scalability. We believe that the performance-limiting bottleneck for many future systems-ona -chip will be same as the one found in many of today's board-level systems: system-wide interconnect. In this paper, a new single-chip interconnection architecture is described that not only provides scalable data transfer but also can be easily reconfigured as communication patterns change. An important aspect of the architecture is its support for compile-time, scheduled communi...

This paper describes a computer architecture, Spatial Computation (SC), which is based on the translation of high-level language programs directly into hardware structures. SC program implementations are completely distributed, with no centralized control. SC circuits are optimized for wires at the expense of computation units. In this paper we investigate a particular implementation of SC: ASH (Application-Specific Hardware). Under the assumption that computation is cheaper than communication, ASH replicates computation units to simplify interconnect, building a system which uses very simple, completely dedicated communication channels. As a consequence, communication on the datapath never requires arbitration; the only arbitration required is for accessing memory. ASH relies on very simple hardware primitives, using no associative structures, no multiported register files, no scheduling logic, no broadcast, and no clocks. As a consequence, ASH hardware is fast and extremely power efficient.

As on-chip networks become prevalent in multiprocessor systemson -a-chip and multi-core processors, they will be an integral part of the design flow of such systems. With power increasingly the primary constraint in chips, the tool chain in systems design, from simulation infrastructures to compilers and synthesis frameworks, needs to take network power into account, motivating the need for early-stage communication power analysis.